├── .github └── workflows │ └── aws_stac_catalogs.yml ├── .gitignore ├── LICENSE ├── README.md ├── aws_stac_catalogs.ipynb ├── aws_stac_catalogs.json ├── aws_stac_catalogs.py ├── aws_stac_catalogs.tsv ├── datasets ├── amazonia.yaml ├── asf-event-data.yaml ├── brazil-data-cubes.yaml ├── capella_opendata.yaml ├── cbers.yaml ├── deafrica-alos-jers.yaml ├── deafrica-chirps.yaml ├── deafrica-crop-extent.yaml ├── deafrica-fractional-cover.yaml ├── deafrica-geomad.yaml ├── deafrica-landsat.yaml ├── deafrica-mangrove.yaml ├── deafrica-ndvi_anomaly.yaml ├── deafrica-ndvi_climatology_ls.yaml ├── deafrica-sentinel-1.yaml ├── deafrica-sentinel-2.yaml ├── deafrica-waterbodies.yaml ├── deafrica-wofs.yaml ├── esa-worldcover-vito-composites.yaml ├── esa-worldcover-vito.yaml ├── glo-30-hand.yaml ├── io-lulc.yaml ├── its-live-data.yaml ├── jaxa-alos-palsar2-scansar.yaml ├── jaxa-usgs-nasa-kaguya-tc-dtms.yaml ├── jaxa-usgs-nasa-kaguya-tc.yaml ├── jaxa-usgs-nasa-kaguya-tc_monoscopic.yaml ├── maxar-open-data.yaml ├── nasa-usgs-controlled-mro-ctx-dtms.yaml ├── nasa-usgs-europa-dtms.yaml ├── nasa-usgs-europa-mosaics.yaml ├── nasa-usgs-europa-observations.yaml ├── nasa-usgs-lunar-orbiter-laser-altimeter.yaml ├── nasa-usgs-mars-hirise-dtms.yaml ├── nasa-usgs-mars-hirise.yaml ├── nasa-usgs-themis-mosaics.yaml ├── nasa-usgs-themis-mosasics.yaml ├── noaa-coastal-lidar.yaml ├── noaa-ufs-coastal-pds.yaml ├── nz-elevation.yaml ├── nz-imagery.yaml ├── palsar-2-scansar-flooding-in-bangladesh.yaml ├── palsar-2-scansar-flooding-in-rwanda.yaml ├── palsar2-scansar-turkey-syria.yaml ├── pgc-arcticdem.yaml ├── pgc-earthdem.yaml ├── pgc-rema.yaml ├── radiant-mlhub.yaml ├── rcm-ceos-ard.yaml ├── satellogic-earthview.yaml ├── sentinel-1-rtc-indigo.yaml ├── sentinel-2-l2a-cogs.yaml ├── sentinel-2.yaml ├── sentinel-3.yaml ├── sentinel-products-ca-mirror.yaml ├── sentinel5p.yaml ├── umbra-open-data.yaml ├── usgs-landsat.yaml ├── usgs-lidar.yaml ├── venus-l2a-cogs.yaml └── wb-light-every-night.yaml └── requirements.txt /.github/workflows/aws_stac_catalogs.yml: -------------------------------------------------------------------------------- 1 | name: aws_catalog 2 | on: 3 | workflow_dispatch: 4 | schedule: 5 | - cron: "25 3 * * *" # https://crontab.guru/ 6 | 7 | jobs: 8 | build: 9 | runs-on: ubuntu-latest 10 | steps: 11 | - name: checkout repo content 12 | uses: actions/checkout@v3 13 | 14 | - name: setup python 15 | uses: actions/setup-python@v4 16 | with: 17 | python-version: "3.10" 18 | 19 | - name: install python packages 20 | run: | 21 | python -m pip install --upgrade pip 22 | pip install -r requirements.txt 23 | - name: execute python script 24 | run: | 25 | python aws_stac_catalogs.py 26 | - name: file_check 27 | run: ls -l -a 28 | - name: commit files 29 | continue-on-error: true 30 | run: | 31 | today=$(date +"%Y-%m-%d") 32 | git config --local user.email "action@github.com" 33 | git config --local user.name "GitHub Action" 34 | git add -A 35 | git commit -m "Updated datasets ${today} UTC" -a 36 | git pull origin master 37 | - name: push changes 38 | continue-on-error: true 39 | uses: ad-m/github-push-action@master 40 | with: 41 | github_token: ${{ secrets.GITHUB_TOKEN }} 42 | branch: master 43 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | open-data-registry-main.zip 10 | open-data-registry-main/ 11 | .vscode/ 12 | 13 | # Distribution / packaging 14 | .Python 15 | build/ 16 | develop-eggs/ 17 | dist/ 18 | downloads/ 19 | eggs/ 20 | .eggs/ 21 | lib/ 22 | lib64/ 23 | parts/ 24 | sdist/ 25 | var/ 26 | wheels/ 27 | pip-wheel-metadata/ 28 | share/python-wheels/ 29 | *.egg-info/ 30 | .installed.cfg 31 | *.egg 32 | MANIFEST 33 | 34 | # PyInstaller 35 | # Usually these files are written by a python script from a template 36 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 37 | *.manifest 38 | *.spec 39 | 40 | # Installer logs 41 | pip-log.txt 42 | pip-delete-this-directory.txt 43 | 44 | # Unit test / coverage reports 45 | htmlcov/ 46 | .tox/ 47 | .nox/ 48 | .coverage 49 | .coverage.* 50 | .cache 51 | nosetests.xml 52 | coverage.xml 53 | *.cover 54 | *.py,cover 55 | .hypothesis/ 56 | .pytest_cache/ 57 | 58 | # Translations 59 | *.mo 60 | *.pot 61 | 62 | # Django stuff: 63 | *.log 64 | local_settings.py 65 | db.sqlite3 66 | db.sqlite3-journal 67 | 68 | # Flask stuff: 69 | instance/ 70 | .webassets-cache 71 | 72 | # Scrapy stuff: 73 | .scrapy 74 | 75 | # Sphinx documentation 76 | docs/_build/ 77 | 78 | # PyBuilder 79 | target/ 80 | 81 | # Jupyter Notebook 82 | .ipynb_checkpoints 83 | 84 | # IPython 85 | profile_default/ 86 | ipython_config.py 87 | 88 | # pyenv 89 | .python-version 90 | 91 | # pipenv 92 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 93 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 94 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 95 | # install all needed dependencies. 96 | #Pipfile.lock 97 | 98 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 99 | __pypackages__/ 100 | 101 | # Celery stuff 102 | celerybeat-schedule 103 | celerybeat.pid 104 | 105 | # SageMath parsed files 106 | *.sage.py 107 | 108 | # Environments 109 | .env 110 | .venv 111 | env/ 112 | venv/ 113 | ENV/ 114 | env.bak/ 115 | venv.bak/ 116 | 117 | # Spyder project settings 118 | .spyderproject 119 | .spyproject 120 | 121 | # Rope project settings 122 | .ropeproject 123 | 124 | # mkdocs documentation 125 | /site 126 | 127 | # mypy 128 | .mypy_cache/ 129 | .dmypy.json 130 | dmypy.json 131 | 132 | # Pyre type checker 133 | .pyre/ 134 | 135 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022-2023, Qiusheng Wu 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # aws-open-data-stac 2 | 3 | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/giswqs/aws-open-data-stac/blob/master/aws_stac_catalogs.ipynb) 4 | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/giswqs/aws-open-data-stac/HEAD?labpath=aws_stac_catalogs.ipynb) 5 | [![License](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) 6 | 7 | ## Introduction 8 | 9 | The [AWS Open Data](https://registry.opendata.aws/) program hosts a lot of publicly available geospatial datasets. Some of these datasets are available as [SpatioTemporal Asset Catalog (STAC)](https://stacspec.org/) endpoints. This repo compiles the list of all AWS Open geospatial datasets with a STAC endpoint as a CSV file and as a JSON file, making it easier to find and use them programmatically. The list is updated daily. 10 | 11 | A complete list of AWS open datasets as individual YAML files is available [here](https://github.com/awslabs/open-data-registry). 12 | 13 | ## Usage 14 | 15 | This repo provides the list of AWS open geospatial datasets with a STAC endpoint in two formats: 16 | 17 | - Tab separated values (TSV) file: [aws_stac_catalogs.tsv](https://github.com/giswqs/aws-open-data-stac/blob/master/aws_stac_catalogs.tsv) 18 | - JSON file: [aws_stac_catalogs.json](https://github.com/giswqs/aws-open-data-stac/blob/master/aws_stac_catalogs.json) 19 | 20 | The TSV file can be easily read into a Pandas DataFrame using the following code: 21 | 22 | ```python 23 | import pandas as pd 24 | 25 | url = 'https://github.com/giswqs/aws-open-data-stac/raw/master/aws_stac_catalogs.tsv' 26 | df = pd.read_csv(url, sep='\t') 27 | df.head() 28 | ``` 29 | 30 | ## Related Projects 31 | 32 | - A list of open datasets on AWS: [aws-open-data](https://github.com/giswqs/aws-open-data) 33 | - A list of open geospatial datasets on AWS: [aws-open-data-geo](https://github.com/giswqs/aws-open-data-geo) 34 | - A list of open geospatial datasets on AWS with a STAC endpoint: [aws-open-data-stac](https://github.com/giswqs/aws-open-data-stac) 35 | - A list of STAC endpoints from stacindex.org: [stac-index-catalogs](https://github.com/giswqs/stac-index-catalogs) 36 | - A list of geospatial datasets on Microsoft Planetary Computer: [Planetary-Computer-Catalog](https://github.com/giswqs/Planetary-Computer-Catalog) 37 | - A list of geospatial datasets on Google Earth Engine: [Earth-Engine-Catalog](https://github.com/giswqs/Earth-Engine-Catalog) 38 | - A list of geospatial datasets on NASA's Common Metadata Repository (CMR): [NASA-CMR-STAC](https://github.com/giswqs/NASA-CMR-STAC) 39 | - A list of geospatial data catalogs: [geospatial-data-catalogs](https://github.com/giswqs/geospatial-data-catalogs) 40 | -------------------------------------------------------------------------------- /aws_stac_catalogs.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "a89a128d-f74e-449f-adb6-e6e217df1c65", 6 | "metadata": {}, 7 | "source": [ 8 | "[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/giswqs/aws-open-data-stac/blob/master/aws_stac_catalogs.ipynb)\n", 9 | "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/giswqs/aws-open-data-stac/HEAD?labpath=aws_stac_catalogs.ipynb)\n", 10 | "[![License](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": null, 16 | "id": "0b655880-c912-48ad-8ade-10d9fe7e1812", 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "import pandas as pd" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": null, 26 | "id": "63171939-2173-4948-ac8b-896d5bbff5c2", 27 | "metadata": {}, 28 | "outputs": [], 29 | "source": [ 30 | "url = 'https://github.com/giswqs/aws-open-data-stac/raw/master/aws_stac_catalogs.tsv'\n", 31 | "df = pd.read_csv(url, sep='\\t')" 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": null, 37 | "id": "15c26beb-c5a1-4d72-8aea-a1399d1970ae", 38 | "metadata": {}, 39 | "outputs": [], 40 | "source": [ 41 | "df" 42 | ] 43 | } 44 | ], 45 | "metadata": { 46 | "kernelspec": { 47 | "display_name": "Python 3", 48 | "language": "python", 49 | "name": "python3" 50 | }, 51 | "language_info": { 52 | "codemirror_mode": { 53 | "name": "ipython", 54 | "version": 3 55 | }, 56 | "file_extension": ".py", 57 | "mimetype": "text/x-python", 58 | "name": "python", 59 | "nbconvert_exporter": "python", 60 | "pygments_lexer": "ipython3", 61 | "version": "3.9.13" 62 | } 63 | }, 64 | "nbformat": 4, 65 | "nbformat_minor": 5 66 | } 67 | -------------------------------------------------------------------------------- /aws_stac_catalogs.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | import shutil 4 | import yaml 5 | import leafmap 6 | import pandas as pd 7 | 8 | url = "https://github.com/awslabs/open-data-registry/archive/refs/heads/main.zip" 9 | out_dir = "open-data-registry-main" 10 | zip_file = "open-data-registry-main.zip" 11 | 12 | if os.path.exists(out_dir): 13 | shutil.rmtree(out_dir) 14 | 15 | if os.path.exists(zip_file): 16 | os.remove(zip_file) 17 | 18 | leafmap.download_file(url, output=zip_file, unzip=True) 19 | 20 | 21 | in_dir = os.path.join(out_dir, "datasets") 22 | 23 | files = leafmap.find_files(in_dir, ext=".yaml") 24 | 25 | print(f"Total number of AWS open datasets: {len(files)}") 26 | 27 | datasets = [] 28 | names = {} 29 | 30 | for file in files: 31 | dataset = {} 32 | with open(file, "r") as f: 33 | dataset = yaml.safe_load(f) 34 | 35 | if "Deprecated" in dataset: 36 | continue 37 | 38 | tags = dataset.get("Tags", []) 39 | name = dataset.get("Name", "") 40 | 41 | if "stac" in tags: 42 | 43 | basename = os.path.basename(file) 44 | out_file = os.path.join("datasets", basename) 45 | 46 | shutil.copy(file, out_file) 47 | 48 | resources = dataset.get("Resources", []) 49 | 50 | for resource in resources: 51 | 52 | if "Explore" in resource: 53 | 54 | names[name] = names.get(name, 0) + 1 55 | 56 | for resource in resources: 57 | 58 | if "Explore" in resource: 59 | explore = resource["Explore"][0] 60 | url = explore[explore.find("http") : -1] 61 | 62 | resource.pop("Explore") 63 | 64 | item = {} 65 | 66 | resource["Description"] = resource["Description"].replace( 67 | "Water Observations from Space ", "" 68 | ) 69 | resource["Description"] = resource["Description"].replace( 70 | "", 71 | "", 72 | ) 73 | resource["Description"] = resource["Description"].replace( 74 | "Scenes and metadata for ", "" 75 | ) 76 | if names[name] > 1: 77 | item["Name"] = ( 78 | f"{name} - {resource['Description'].replace(name, '')}" 79 | ) 80 | else: 81 | item["Name"] = name 82 | 83 | item["Name"] = item["Name"].replace("/", "-").replace("- -", "-") 84 | item["Name"] = item["Name"].split(".")[0] 85 | item["Name"] = item["Name"].split(",")[0] 86 | item["Name"] = item["Name"].split("|")[0] 87 | item["Name"] = item["Name"].replace("JAXA - USGS - ", "") 88 | item["Name"] = item["Name"].replace( 89 | "ESA WorldCover Sentinel-1 and Sentinel-2 10m Annual Composites - ", 90 | "", 91 | ) 92 | 93 | item["Endpoint"] = url 94 | 95 | for key in resource: 96 | item[key] = resource[key] 97 | 98 | datasets.append(item) 99 | 100 | 101 | print(f"Total number of STAC datasets: {len(datasets)}") 102 | 103 | df = pd.DataFrame(datasets) 104 | df = df.sort_values(by="Name") 105 | df.to_csv("aws_stac_catalogs.tsv", index=False, sep="\t") 106 | 107 | data = json.loads(df.to_json(orient="records")) 108 | 109 | with open("aws_stac_catalogs.json", "w") as f: 110 | json.dump(data, f, indent=4) 111 | -------------------------------------------------------------------------------- /datasets/amazonia.yaml: -------------------------------------------------------------------------------- 1 | Name: Amazonia EO satellite on AWS 2 | Description: | 3 | Imagery acquired 4 | by Amazonia-1 satellite. 5 | The 6 | image files are recorded and processed by Instituto Nacional de Pesquisas 7 | Espaciais (INPE) and are converted to Cloud Optimized Geotiff 8 | format in order to optimize its use for cloud based applications. 9 | WFI Level 4 (Orthorectified) scenes are being 10 | ingested daily starting from 08-29-2022, the complete 11 | Level 4 archive will be ingested by the end of October 2022. 12 | Documentation: http://www.inpe.br/amazonia1 13 | Contact: https://lists.osgeo.org/mailman/listinfo/cbers-pds 14 | ManagedBy: "[Frederico Liporace](https://github.com/fredliporace)" 15 | UpdateFrequency: Daily 16 | Collabs: 17 | ASDI: 18 | Tags: 19 | - satellite imagery 20 | Tags: 21 | - aws-pds 22 | - agriculture 23 | - earth observation 24 | - geospatial 25 | - imaging 26 | - satellite imagery 27 | - sustainability 28 | - disaster response 29 | - stac 30 | - cog 31 | License: https://creativecommons.org/licenses/by-sa/3.0/ 32 | Resources: 33 | - Description: Amazonia 1 imagery (COG files, quicklooks, metadata) 34 | ARN: arn:aws:s3:::brazil-eosats 35 | Region: us-west-2 36 | Type: S3 Bucket 37 | RequesterPays: False 38 | Explore: 39 | - '[STAC V1.0.0 endpoint](https://stac.scitekno.com.br/v100)' 40 | - '[stacindex](https://stacindex.org/catalogs/cbers)' 41 | - Description: STAC static catalog 42 | ARN: arn:aws:s3:::br-eo-stac-1-0-0 43 | Region: us-west-2 44 | Type: S3 Bucket 45 | RequesterPays: False 46 | - Description: Notifications for new quicklooks 47 | ARN: arn:aws:sns:us-west-2:599544552497:NewAM1Quicklook 48 | Region: us-west-2 49 | Type: SNS Topic 50 | - Description: Topic that receives STAC V1.0.0 items as new scenes are ingested 51 | ARN: arn:aws:sns:us-west-2:769537946825:br-eo-stac-prod-stacitemtopic4BCE3141-Z8he7LYjqXFe 52 | Region: us-west-2 53 | Type: SNS Topic 54 | DataAtWork: 55 | Tutorials: 56 | - Title: Keeping a SpatioTemporal Asset Catalog (STAC) Up To Date with SNS/SQS 57 | URL: https://aws.amazon.com/blogs/publicsector/keeping-a-spatiotemporal-asset-catalog-stac-up-to-date-with-sns-sqs/ 58 | AuthorName: Frederico Liporace 59 | Services: 60 | - Amazon SNS 61 | - AWS Lambda 62 | - Amazon DynamoDB 63 | - Title: The Evolution of ASDI's Data Infrastructure 64 | URL: https://developmentseed.org/blog/2023-10-20-asdi 65 | AuthorName: Sean Harkins 66 | Services: 67 | - Amazon SNS 68 | - AWS Lambda 69 | - Amazon ECR 70 | - Amazon S3 71 | - Amazon Athena 72 | Tools & Applications: 73 | - Title: STAC V1.0.0 endpoint 74 | URL: https://stac.scitekno.com.br/v100 75 | AuthorName: Frederico Liporace 76 | AuthorURL: https://github.com/fredliporace 77 | - Title: Amazonia 1 stactools package 78 | URL: https://github.com/stactools-packages/amazonia-1 79 | AuthorName: Frederico Liporace 80 | AuthorURL: https://github.com/fredliporace 81 | - Title: Amazonia 1 stactools-pipeline 82 | URL: https://github.com/developmentseed/stactools-pipelines/pull/33 83 | AuthorName: Frederico Liporace 84 | AuthorURL: https://github.com/fredliporace 85 | 86 | -------------------------------------------------------------------------------- /datasets/asf-event-data.yaml: -------------------------------------------------------------------------------- 1 | Name: ASF SAR Data Products for Disaster Events 2 | Description: > 3 | synthetic Aperture Radar (SAR) data is a powerful tool for monitoring and assessing disaster events and can provide 4 | valuable insights for researchers, scientists, and emergency response teams. 5 | 6 | The Alaska Satellite Facility (ASF) curates this collection of (primarily) SAR and SAR-derived satellite data products 7 | from a variety of data sources for disaster events. 8 | Documentation: https://asf-event-data.s3.us-west-2.amazonaws.com/README.md 9 | Contact: https://asf.alaska.edu/asf/contact-us/ 10 | ManagedBy: "[The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/)" 11 | UpdateFrequency: > 12 | Irregular, in response to disaster events 13 | Tags: 14 | - aws-pds 15 | - disaster response 16 | - satellite imagery 17 | - geospatial 18 | - cog 19 | - stac 20 | License: This data falls under the terms and conditions of the [Creative Commons Zero (CC0) 1.0 Universal License](https://creativecommons.org/publicdomain/zero/1.0/) unless otherwise noted. 21 | Citation: 22 | Resources: 23 | - Description: ASF Event data S3 bucket 24 | ARN: arn:aws:s3:::asf-event-data 25 | Region: us-west-2 26 | Type: S3 Bucket 27 | Explore: 28 | - '[Browse Bucket](https://asf-event-data.s3.amazonaws.com/index.html)' 29 | - Description: Notifications for new event data 30 | ARN: arn:aws:sns:us-west-2:654654592981:asf-event-data-object_created 31 | Region: us-west-2 32 | Type: SNS Topic 33 | DataAtWork: 34 | Tools & Applications: 35 | - Title: ASF Event Search 36 | URL: https://search.asf.alaska.edu/#/?searchType=Event%20Search 37 | AuthorName: Alaska Satellite Facility 38 | AuthorURL: https://asf.alaska.edu/ 39 | Publications: 40 | - Title: Event Search Manual 41 | URL: https://docs.asf.alaska.edu/vertex/events/ 42 | AuthorName: Alaska Satellite Facility 43 | AuthorURL: https://asf.alaska.edu/ 44 | - Title: SARVIEWS Events Collection 45 | URL: https://docs.asf.alaska.edu/datasets/events_about/ 46 | AuthorName: Alaska Satellite Facility 47 | AuthorURL: https://asf.alaska.edu/ 48 | DeprecatedNotice: 49 | ADXCategories: 50 | - Environmental Data 51 | -------------------------------------------------------------------------------- /datasets/brazil-data-cubes.yaml: -------------------------------------------------------------------------------- 1 | Name: Earth Observation Data Cubes for Brazil 2 | Description: "Earth observation (EO) data cubes produced from analysis-ready data (ARD) of CBERS-4, Sentinel-2 A/B and Landsat-8 satellite images for Brazil. The datacubes are regular in time and use a hierarchical tiling system. Further details are described in [Ferreira et al. (2020)](https://www.mdpi.com/2072-4292/12/24/4033)." 3 | Documentation: http://brazildatacube.org/en/home-page-2/ 4 | Contact: brazildatacube@inpe.br 5 | ManagedBy: "[INPE - Brazil Data Cube](http://brazildatacube.org/)" 6 | UpdateFrequency: New EO data cubes are added as soon as there are produced by the Brazil Data Cube project. 7 | DeprecatedNotice: This dataset is deprecated and will be removed from AWS Open Data in the near future. If you have any questions or require assistance, please contact us at [data.support@inpe.br]. 8 | Tags: 9 | - earth observation 10 | - satellite imagery 11 | - geoscience 12 | - geospatial 13 | - image processing 14 | - open source software 15 | - cog 16 | - stac 17 | - aws-pds 18 | License: | 19 | The EO data cubes are produced from free and open images of CBERS-4, Landsat-8 and Sentinel-2 satellites. Data usage is subject to Terms and Conditions for the use and distribution of [Landsat data](https://www.usgs.gov/centers/eros/data-citation?qt-science_support_page_related_con=0#qt-science_support_page_related_con), Legal Notice on the use and distribution of [Sentinel-2 data](https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice/) and License for use and distribution of [CBERS data](https://creativecommons.org/licenses/by-sa/3.0/). When using data produced by the Brazil Data Cube, please acknowledge the work of the Brazilian National Institute for Space Research (INPE) in producing such data. 20 | CBERS-4 image courtesy of the National Institute for Space Research (INPE). 21 | Landsat-8 image courtesy of the U.S. Geological Survey. 22 | Sentinel-2 (ESA) image courtesy of the Copernicus SciHub. 23 | Sentinel-2 Cloud-Optimized GeoTIFFs data 2017-2018, was accessed on June/2021 from https://registry.opendata.aws/sentinel-2-l2a-cogs. 24 | Resources: 25 | - Description: Earth Observation Data Cubes for Brazil - Sentinel 2A/2B 26 | ARN: arn:aws:s3:::bdc-sentinel-2 27 | Region: us-west-2 28 | Type: S3 Bucket 29 | RequesterPays: False 30 | Explore: 31 | - '[BDC STAC V0.9.0 endpoint](https://bdc-sentinel-2.s3.us-west-2.amazonaws.com/catalog.json)' 32 | - Description: Earth Observation Data Cubes for Brazil - CBERS 4 33 | ARN: arn:aws:s3:::bdc-cbers 34 | Region: us-west-2 35 | Type: S3 Bucket 36 | RequesterPays: False 37 | Explore: 38 | - '[BDC STAC V0.9.0 endpoint](https://bdc-cbers.s3.us-west-2.amazonaws.com/catalog.json)' 39 | - Description: Notifications for new EO Data Cubes Sentinel-2 scenes 40 | ARN: arn:aws:sns:us-west-2:896627514407:bdc-sentinel-2-object_created 41 | Region: us-west-2 42 | Type: SNS Topic 43 | - Description: Notifications for new EO Data Cubes CBERS scenes 44 | ARN: arn:aws:sns:us-west-2:896627514407:bdc-cbers-object_created 45 | Region: us-west-2 46 | Type: SNS Topic 47 | DataAtWork: 48 | Tutorials: 49 | - Title: Using Python to Access Image Collections Data (Jupyter notebook) 50 | URL: https://github.com/brazil-data-cube/code-gallery/blob/master/jupyter/Python/stac/stac-aws-introduction.ipynb 51 | AuthorName: Brazil Data Cube 52 | AuthorURL: https://brazil-data-cube.github.io/ 53 | - Title: Tile Map Service to view Image Collections from Brazil Data Cube Catalog 54 | URL: https://github.com/brazil-data-cube/code-gallery/blob/master/jupyter/Python/tiler/bdc-tiler_introduction.ipynb 55 | AuthorName: Brazil Data Cube 56 | AuthorURL: https://brazil-data-cube.github.io 57 | Tools & Applications: 58 | - Title: rstac - R library to query and download Image Collections from Brazil Data Cube Catalog on Amazon S3 59 | URL: https://github.com/brazil-data-cube/rstac 60 | AuthorName: Brazil Data Cube 61 | AuthorURL: https://brazil-data-cube.github.io 62 | - Title: cube-builder-aws - Application to generate data cubes on AWS environment. 63 | URL: https://github.com/brazil-data-cube/cube-builder-aws 64 | AuthorName: Brazil Data Cube 65 | AuthorURL: https://brazil-data-cube.github.io 66 | Publications: 67 | - Title: "Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products." 68 | URL: https://www.mdpi.com/2072-4292/12/24/4033 69 | AuthorName: K. R. Ferreira, et al. 70 | - Title: Using Remote Sensing Images and Cloud Services on AWS to Improve Land Use and Cover Monitoring 71 | URL: https://ieeexplore.ieee.org/abstract/document/9165649 72 | AuthorName: K. R. Ferreira, et al. 73 | - Title: Building Earth Observation Data Cubes on AWS 74 | URL: https://www.proquest.com/openview/070d2a753cc88d26535c98293171a5ac/1? 75 | AuthorName: Ferreira, K R; Queiroz, G R; Marujo, R F B; Costa, R W.  76 | -------------------------------------------------------------------------------- /datasets/capella_opendata.yaml: -------------------------------------------------------------------------------- 1 | Name: Capella Space Synthetic Aperture Radar (SAR) Open Dataset 2 | Description: | 3 | Open Synthetic Aperture Radar (SAR) data from Capella Space. Capella Space is an information services company 4 | that provides on-demand, industry-leading, high-resolution synthetic aperture radar (SAR) Earth observation 5 | imagery. Through a constellation of small satellites, Capella provides easy access to frequent, timely, and 6 | flexible information affecting dozens of industries worldwide. Capella's high-resolution SAR satellites are 7 | matched with unparalleled infrastructure to deliver reliable global insights that sharpen our understanding 8 | of the changing world – improving decisions about commerce, conservation, and security on Earth. Learn more 9 | at www.capellaspace.com. 10 | Documentation: Documentation is available under [support.capellaspace.com](https://support.capellaspace.com/) 11 | Contact: opendata@capellaspace.com 12 | ManagedBy: "[Capella Space](https://www.capellaspace.com/)" 13 | UpdateFrequency: New data is added quarterly. 14 | Collabs: 15 | ASDI: 16 | Tags: 17 | - satellite imagery 18 | Tags: 19 | - aws-pds 20 | - cog 21 | - stac 22 | - earth observation 23 | - satellite imagery 24 | - geospatial 25 | - image processing 26 | - computer vision 27 | - synthetic aperture radar 28 | License: | 29 | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) 30 | Resources: 31 | - Description: Capella Space Open Data in COG format 32 | ARN: arn:aws:s3:::capella-open-data/data/ 33 | Region: us-west-2 34 | Type: S3 Bucket 35 | RequesterPays: False 36 | Explore: 37 | - '[STAC Catalog](https://capella-open-data.s3.us-west-2.amazonaws.com/stac/catalog.json)' 38 | - '[STAC Browser](https://radiantearth.github.io/stac-browser/#/external/capella-open-data.s3.us-west-2.amazonaws.com/stac/catalog.json)' 39 | - Description: Capella Space Open Data in TileDB format 40 | ARN: arn:aws:s3:::capella-open-data/data/tiledb/ 41 | Region: us-west-2 42 | Type: S3 Bucket 43 | RequesterPays: False 44 | DataAtWork: 45 | Tutorials: 46 | - Title: Scaling GEO Images in QGIS 47 | URL: https://support.capellaspace.com/hc/en-us/articles/7124025490964-Scaling-GEO-Images-in-QGIS 48 | AuthorName: Capella Space 49 | AuthorURL: https://www.capellaspace.com/ 50 | Tools & Applications: 51 | - Title: Python SDK for api.capellaspace.com 52 | URL: https://capella-console-client.readthedocs.io/en/main/ 53 | AuthorName: Capella Space 54 | AuthorURL: https://www.capellaspace.com/ 55 | - Title: Single Look Complex data reader for Capella SLC images - python module to convert Capella SLC data into an amplitude image. 56 | URL: https://github.com/capellaspace/tools/tree/master/capella-slc-reader 57 | AuthorName: Capella Space 58 | AuthorURL: https://www.capellaspace.com/ 59 | Publications: 60 | - Title: Radar Generalized Image Quality Equation Applied to Capella Open Dataset 61 | URL: https://ieeexplore.ieee.org/document/9764201/ 62 | AuthorName: Wade Schwartzkopf, Jason Brown, Gordon Farquharson, Craig Stringham, Michael Duersch, Jordan Heemskerk 63 | - Title: Analyzing LiDAR and SAR data with Capella Space and TileDB 64 | URL: https://tiledb.com/blog/analyzing-lidar-and-sar-data-with-capella-space-and-tiledb 65 | AuthorName: Stavros Papadopoulos 66 | - Title: Open SAR data and scalable analytics 67 | URL: https://medium.com/tiledb/open-sar-data-and-scalable-analytics-bfd1a5257e9 68 | AuthorName: Norman Barker 69 | -------------------------------------------------------------------------------- /datasets/cbers.yaml: -------------------------------------------------------------------------------- 1 | Name: CBERS on AWS 2 | Description: | 3 | Imagery acquired 4 | by the China-Brazil Earth Resources Satellite (CBERS), 4 and 4A. 5 | The 6 | image files are recorded and processed by Instituto Nacional de Pesquisas 7 | Espaciais (INPE) and are converted to Cloud Optimized Geotiff 8 | format in order to optimize its use for cloud based applications. 9 | Contains all CBERS-4 MUX, AWFI, PAN5M and 10 | PAN10M scenes acquired since 11 | the start of the satellite mission and is daily updated with 12 | new scenes. 13 | CBERS-4A MUX Level 4 (Orthorectified) scenes are being 14 | ingested starting from 04-13-2021. CBERS-4A WFI Level 4 (Orthorectified) 15 | scenes are being ingested starting from 10-12-2022. 16 | CBERS-4A WPM Level 4 (Orthorectified) scenes are being ingested starting from 03-27-2022. 17 | Documentation: https://github.com/fredliporace/cbers-on-aws 18 | Contact: https://lists.osgeo.org/mailman/listinfo/cbers-pds 19 | ManagedBy: "[Frederico Liporace](https://github.com/fredliporace)" 20 | UpdateFrequency: Daily 21 | Collabs: 22 | ASDI: 23 | Tags: 24 | - satellite imagery 25 | Tags: 26 | - aws-pds 27 | - agriculture 28 | - earth observation 29 | - geospatial 30 | - imaging 31 | - satellite imagery 32 | - disaster response 33 | - stac 34 | - cog 35 | License: https://creativecommons.org/licenses/by-sa/3.0/ 36 | Resources: 37 | - Description: CBERS imagery (COG files, quicklooks, metadata) 38 | ARN: arn:aws:s3:::brazil-eosats 39 | Region: us-west-2 40 | Type: S3 Bucket 41 | RequesterPays: False 42 | Explore: 43 | - '[STAC V1.0.0 endpoint](https://stac.scitekno.com.br/v100)' 44 | - '[stacindex](https://stacindex.org/catalogs/cbers)' 45 | - Description: STAC static catalog 46 | ARN: arn:aws:s3:::br-eo-stac-1-0-0 47 | Region: us-west-2 48 | Type: S3 Bucket 49 | RequesterPays: False 50 | - Description: Notifications for new CBERS 4A quicklooks, all sensors 51 | ARN: arn:aws:sns:us-west-2:599544552497:NewCB4AQuicklook 52 | Region: us-west-2 53 | Type: SNS Topic 54 | - Description: Notifications for new CBERS 4 quicklooks, all sensors 55 | ARN: arn:aws:sns:us-west-2:599544552497:NewCB4Quicklook 56 | Region: us-west-2 57 | Type: SNS Topic 58 | - Description: Topic that receives STAC V1.0.0 items as new scenes are ingested 59 | ARN: arn:aws:sns:us-west-2:769537946825:br-eo-stac-prod-stacitemtopic4BCE3141-Z8he7LYjqXFe 60 | Region: us-west-2 61 | Type: SNS Topic 62 | DataAtWork: 63 | Tutorials: 64 | - Title: Keeping a SpatioTemporal Asset Catalog (STAC) Up To Date with SNS/SQS 65 | URL: https://aws.amazon.com/blogs/publicsector/keeping-a-spatiotemporal-asset-catalog-stac-up-to-date-with-sns-sqs/ 66 | AuthorName: Frederico Liporace 67 | Services: 68 | - Amazon SNS 69 | - AWS Lambda 70 | - Amazon DynamoDB 71 | Tools & Applications: 72 | - Title: STAC V1.0.0 endpoint 73 | URL: https://stac.scitekno.com.br/v100 74 | AuthorName: Scitekno 75 | AuthorURL: https://github.com/fredliporace/cbers-2-stac 76 | - Title: EOS Land Viewer 77 | URL: https://eos.com/landviewer/ 78 | AuthorName: Earth Observing System 79 | AuthorURL: https://eos.com/ 80 | - Title: CBERS timelapse GIF generator 81 | URL: https://github.com/fredliporace/cbersgif 82 | AuthorName: Frederico Liporace 83 | AuthorURL: https://github.com/fredliporace 84 | - Title: aws-sat-api-py 85 | URL: https://github.com/RemotePixel/aws-sat-api-py 86 | AuthorName: Remote Pixel 87 | AuthorURL: http://remotepixel.ca/ 88 | - Title: rio-tiler 89 | URL: https://github.com/mapbox/rio-tiler 90 | AuthorName: Mapbox 91 | AuthorURL: https://www.mapbox.com/ 92 | - Title: cbers-tiler 93 | URL: https://github.com/mapbox/cbers-tiler 94 | AuthorName: Mapbox 95 | AuthorURL: https://www.mapbox.com/ 96 | - Title: CBERS static STAC catalog served by stac-browser 97 | URL: https://cbers.stac.cloud 98 | AuthorName: Radiant Earth 99 | AuthorURL: https://github.com/radiantearth/stac-browser 100 | - Title: Forest Monitor 101 | URL: http://brazildatacube.dpi.inpe.br/forest-monitor/ 102 | AuthorName: Brazil Datacube, INPE 103 | AuthorURL: http://brazildatacube.org/ 104 | Publications: 105 | - Title: Using Remote Sensing Images and Cloud Services on AWS to Improve Land Use and Cover Monitoring 106 | URL: https://ieeexplore.ieee.org/abstract/document/9165649 107 | AuthorName: K. R. Ferreira, et al. 108 | -------------------------------------------------------------------------------- /datasets/deafrica-alos-jers.yaml: -------------------------------------------------------------------------------- 1 | Name: Digital Earth Africa ALOS PALSAR, ALOS-2 PALSAR-2 and JERS-1 2 | Description: | 3 | The ALOS/PALSAR annual mosaic is a global 25 m resolution dataset that combines data from many images captured by JAXA’s PALSAR and PALSAR-2 sensors on ALOS-1 and ALOS-2 satellites respectively. This product contains radar measurement in L-band and in HH and HV polarizations. It has a spatial resolution of 25 m and is available annually for 2007 to 2010 (ALOS/PALSAR) and 2015 to 2020 (ALOS-2/PALSAR-2). 4 | The JERS annual mosaic is generated from images acquired by the SAR sensor on the Japanese Earth Resources Satellite-1 (JERS-1) satellite. This product contains radar measurement in L-band and HH polarization. It has a spatial resolution of 25 m and is available for 1996. 5 | This mosaic data is part of a global dataset provided by the Japan Aerospace Exploration Agency (JAXA) Earth Observation Research Center. 6 | Documentation: https://docs.digitalearthafrica.org/en/latest/data_specs/ALOS_PALSAR_annual_mosaic_specs.html 7 | Contact: helpdesk@digitalearthafrica.org 8 | ManagedBy: "[Digital Earth Africa](https://www.digitalearthafrica.org/)" 9 | UpdateFrequency: As available, generally annually. 10 | Collabs: 11 | ASDI: 12 | Tags: 13 | - satellite imagery 14 | Tags: 15 | - aws-pds 16 | - agriculture 17 | - earth observation 18 | - satellite imagery 19 | - geospatial 20 | - natural resource 21 | - disaster response 22 | - synthetic aperture radar 23 | - deafrica 24 | - stac 25 | - cog 26 | License: | 27 | Data is available for free under the [terms of use](https://earth.jaxa.jp/policy/en.html). 28 | Resources: 29 | - Description: ALOS PALSAR ALOS-2 PALSAR-2 data 30 | ARN: arn:aws:s3:::deafrica-input-datasets/alos_palsar_mosaic 31 | Region: af-south-1 32 | Type: S3 Bucket 33 | RequesterPays: False 34 | Explore: 35 | - '[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/alos_palsar_mosaic)' 36 | - Description: "[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents)" 37 | ARN: arn:aws:s3:::deafrica-input-datasets-inventory 38 | Region: af-south-1 39 | Type: S3 Bucket 40 | - Description: Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent contain all object creation events. 41 | ARN: arn:aws:sns:af-south-1:543785577597:deafrica-input-datasets-topic 42 | Region: af-south-1 43 | Type: SNS Topic 44 | DataAtWork: 45 | Tutorials: 46 | - Title: Digital Earth Africa Training 47 | URL: http://learn.digitalearthafrica.org/ 48 | AuthorName: Digital Earth Africa Contributors 49 | Tools & Applications: 50 | - Title: "Digital Earth Africa Explorer (ALOS PALSAR and ALOS-2 PALSAR-2)" 51 | URL: https://explorer.digitalearth.africa/products/alos_palsar_mosaic 52 | AuthorName: Digital Earth Africa Contributors 53 | - Title: "Digital Earth Africa Explorer (JERS)" 54 | URL: https://explorer.digitalearth.africa/products/jers_sar_mosaic 55 | AuthorName: Digital Earth Africa Contributors 56 | - Title: "Digital Earth Africa web services" 57 | URL: https://ows.digitalearth.africa 58 | AuthorName: Digital Earth Africa Contributors 59 | - Title: "Digital Earth Africa Map" 60 | URL: https://maps.digitalearth.africa/ 61 | AuthorName: Digital Earth Africa Contributors 62 | - Title: "Digital Earth Africa Sandbox" 63 | URL: https://sandbox.digitalearth.africa/ 64 | AuthorName: Digital Earth Africa Contributors 65 | - Title: "Digital Earth Africa Notebook Repo" 66 | URL: https://github.com/digitalearthafrica/deafrica-sandbox-notebooks 67 | AuthorName: Digital Earth Africa Contributors 68 | - Title: "Digital Earth Africa Geoportal" 69 | URL: https://www.africageoportal.com/pages/digital-earth-africa 70 | AuthorName: Digital Earth Africa Contributors 71 | Publications: 72 | - Title: "Introduction to DE Africa" 73 | URL: https://youtu.be/Wkf7N6O9jJQ 74 | AuthorName: Dr Fang Yuan 75 | -------------------------------------------------------------------------------- /datasets/deafrica-chirps.yaml: -------------------------------------------------------------------------------- 1 | Name: Digital Earth Africa CHIRPS Rainfall 2 | Description: | 3 | Digital Earth Africa (DE Africa) provides free and open access to a copy of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) monthly and daily products over Africa. The CHIRPS rainfall maps are produced and provided by the Climate Hazards Center in collaboration with the US Geological Survey, and use both rain gauge and satellite observations. 4 | The CHIRPS-2.0 Africa Monthly dataset is regularly indexed to DE Africa from the CHIRPS monthly data. The CHIRPS-2.0 Africa Daily dataset is likewise indexed from the CHIRPS daily data. Both products have been converted to cloud-opitmized GeoTIFFs, and can be accessed through DE Africa’s Open Data Cube. This means the full archive of CHIRPS daily and monthly rainfall can be easily used for inspection or analysis across DE Africa platforms, including the user-interactive DE Africa Map. 5 | For more information on the dataset, see the [CHIRPS website](https://www.chc.ucsb.edu/data/chirps). 6 | Documentation: https://docs.digitalearthafrica.org/en/latest/data_specs/CHIRPS_specs.html 7 | Contact: helpdesk@digitalearthafrica.org 8 | ManagedBy: "[Digital Earth Africa](https://www.digitalearthafrica.org/)" 9 | UpdateFrequency: Monthly. 10 | Collabs: 11 | ASDI: 12 | Tags: 13 | - satellite imagery 14 | Tags: 15 | - aws-pds 16 | - agriculture 17 | - climate 18 | - earth observation 19 | - food security 20 | - geospatial 21 | - meteorological 22 | - satellite imagery 23 | - sustainability 24 | - deafrica 25 | - stac 26 | - cog 27 | License: | 28 | To the extent possible under the law, Pete Peterson has waived all copyright and related or neighboring rights to CHIRPS. CHIRPS data is in the public domain as registered with Creative Commons. 29 | Resources: 30 | - Description: CHIRPS daily rainfall 31 | ARN: arn:aws:s3:::deafrica-input-datasets/rainfall_chirps_daily 32 | Region: af-south-1 33 | Type: S3 Bucket 34 | RequesterPays: False 35 | Explore: 36 | - '[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/rainfall_chirps_daily)' 37 | - Description: CHIRPS monthly rainfall 38 | ARN: arn:aws:s3:::deafrica-input-datasets/rainfall_chirps_monthly 39 | Region: af-south-1 40 | Type: S3 Bucket 41 | RequesterPays: False 42 | Explore: 43 | - '[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/rainfall_chirps_monthly)' 44 | - Description: "[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents)" 45 | ARN: arn:aws:s3:::deafrica-input-datasets-inventory 46 | Region: af-south-1 47 | Type: S3 Bucket 48 | - Description: Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent contain all object creation events. 49 | ARN: arn:aws:sns:af-south-1:543785577597:deafrica-input-datasets-topic 50 | Region: af-south-1 51 | Type: SNS Topic 52 | DataAtWork: 53 | Tutorials: 54 | - Title: "Rainfall - Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS)" 55 | URL: https://docs.digitalearthafrica.org/en/latest/sandbox/notebooks/Datasets/Rainfall_CHIRPS.html 56 | AuthorName: Digital Earth Africa Contributors 57 | - Title: Digital Earth Africa Training 58 | URL: http://learn.digitalearthafrica.org/ 59 | AuthorName: Digital Earth Africa Contributors 60 | Tools & Applications: 61 | - Title: "Digital Earth Africa Explorer (CHIRPS daily rainfall)" 62 | URL: https://explorer.digitalearth.africa/products/rainfall_chirps_daily 63 | AuthorName: Digital Earth Africa Contributors 64 | - Title: "Digital Earth Africa Explorer (CHIRPS monthly rainfall)" 65 | URL: https://explorer.digitalearth.africa/products/rainfall_chirps_monthly 66 | AuthorName: Digital Earth Africa Contributors 67 | - Title: "Digital Earth Africa web services" 68 | URL: https://ows.digitalearth.africa 69 | AuthorName: Digital Earth Africa Contributors 70 | - Title: "Digital Earth Africa Map" 71 | URL: https://maps.digitalearth.africa/ 72 | AuthorName: Digital Earth Africa Contributors 73 | - Title: "Digital Earth Africa Sandbox" 74 | URL: https://sandbox.digitalearth.africa/ 75 | AuthorName: Digital Earth Africa Contributors 76 | - Title: "Digital Earth Africa Notebook Repo" 77 | URL: https://github.com/digitalearthafrica/deafrica-sandbox-notebooks 78 | AuthorName: Digital Earth Africa Contributors 79 | - Title: "Digital Earth Africa Geoportal" 80 | URL: https://www.africageoportal.com/pages/digital-earth-africa 81 | AuthorName: Digital Earth Africa Contributors 82 | Publications: 83 | - Title: "Introduction to DE Africa" 84 | URL: https://youtu.be/Wkf7N6O9jJQ 85 | AuthorName: Dr Fang Yuan 86 | - Title: "The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes" 87 | URL: https://www.nature.com/articles/sdata201566 88 | AuthorName: Chris Funk, Pete Peterson, Martin Landsfeld, Diego Pedreros, James Verdin, Shraddhanand Shukla, Gregory Husak, James Rowland, Laura Harrison, Andrew Hoell and Joel Michaelsen 89 | 90 | 91 | -------------------------------------------------------------------------------- /datasets/deafrica-crop-extent.yaml: -------------------------------------------------------------------------------- 1 | Name: Digital Earth Africa Cropland Extent Map (2019) 2 | Description: | 3 | Digital Earth Africa's cropland extent map (2019) shows the estimated location of croplands in Africa for the period January to December 2019. Cropland is defined as: "a piece of land of minimum 0.01 ha (a single 10m x 10m pixel) that is sowed/planted and harvest-able at least once within the 12 months after the sowing/planting date." This definition will exclude non-planted grazing lands and perennial crops which can be difficult for satellite imagery to differentiate from natural vegetation. 4 | This provisional cropland extent map has a resolution of 10m, and was built using Copernicus Sentinel-2 satellite images from 2019. The cropland extent map was produced using extensive training data from regions across Africa, coupled with a Random Forest machine learning model. The continental service contains maps built separately for eight Agro-Ecological Zones (AEZs). For a detailed exploration of the methods used to produce the cropland extent map, read the Jupyter Notebooks in DE Africa’s crop-mask GitHub repository. 5 | Documentation: https://docs.digitalearthafrica.org/en/latest/data_specs/Cropland_extent_specs.html 6 | Contact: helpdesk@digitalearthafrica.org 7 | ManagedBy: "[Digital Earth Africa](https://www.digitalearthafrica.org/)" 8 | UpdateFrequency: To be defined. 9 | Collabs: 10 | ASDI: 11 | Tags: 12 | - satellite imagery 13 | Tags: 14 | - aws-pds 15 | - agriculture 16 | - earth observation 17 | - food security 18 | - geospatial 19 | - satellite imagery 20 | - sustainability 21 | - deafrica 22 | - stac 23 | - cog 24 | License: | 25 | DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/. 26 | Resources: 27 | - Description: Cropland extent map 2019 28 | ARN: arn:aws:s3:::deafrica-services/crop_mask 29 | Region: af-south-1 30 | Type: S3 Bucket 31 | RequesterPays: False 32 | Explore: 33 | - '[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/crop_mask)' 34 | - Description: "[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents)" 35 | ARN: arn:aws:s3:::deafrica-services-inventory 36 | Region: af-south-1 37 | Type: S3 Bucket 38 | - Description: Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent by the s3 bucket for all object create events. 39 | ARN: arn:aws:sns:af-south-1:543785577597:deafrica-services-topic 40 | Region: af-south-1 41 | Type: SNS Topic 42 | DataAtWork: 43 | Tutorials: 44 | - Title: Digital Earth Africa Training 45 | URL: http://learn.digitalearthafrica.org/ 46 | AuthorName: Digital Earth Africa Contributors 47 | Tools & Applications: 48 | - Title: "Digital Earth Africa Explorer (Cropland Extent Map)" 49 | URL: https://explorer.digitalearth.africa/products/crop_mask 50 | AuthorName: Digital Earth Africa Contributors 51 | - Title: "Digital Earth Africa web services" 52 | URL: https://ows.digitalearth.africa 53 | AuthorName: Digital Earth Africa Contributors 54 | - Title: "Digital Earth Africa Map" 55 | URL: https://maps.digitalearth.africa/ 56 | AuthorName: Digital Earth Africa Contributors 57 | - Title: "Digital Earth Africa Sandbox" 58 | URL: https://sandbox.digitalearth.africa/ 59 | AuthorName: Digital Earth Africa Contributors 60 | - Title: "Digital Earth Africa Notebook Repo" 61 | URL: https://github.com/digitalearthafrica/deafrica-sandbox-notebooks 62 | AuthorName: Digital Earth Africa Contributors 63 | - Title: "Digital Earth Africa Geoportal" 64 | URL: https://www.africageoportal.com/pages/digital-earth-africa 65 | AuthorName: Digital Earth Africa Contributors 66 | Publications: 67 | - Title: "Cropland Extent is now available for the entire African continent" 68 | URL: https://www.digitalearthafrica.org/media-center/blog/cropland-extent-now-available-entire-african-continent 69 | AuthorName: Digital Earth Africa Contributors 70 | - Title: "Co-Production of a 10-m Cropland Extent Map for Continental Africa using Sentinel-2, Cloud Computing, and the Open-Data-Cube" 71 | URL: https://agu2021fallmeeting-agu.ipostersessions.com/Default.aspx?s=BA-E4-89-84-15-50-25-89-AE-A8-EF-C5-32-7D-19-EE 72 | AuthorName: Chad Burton, Fang Yuan, Chong Ee-Faye, Meghan Halabisky, David Ongo, Fatou Mar, Victor Addabor, Bako Mamane and Sena Adimou 73 | 74 | 75 | -------------------------------------------------------------------------------- /datasets/deafrica-fractional-cover.yaml: -------------------------------------------------------------------------------- 1 | Name: Digital Earth Africa Fractional Cover 2 | Description: | 3 | Fractional cover (FC) describes the landscape in terms of coverage by green vegetation, non-green vegetation (including deciduous trees during autumn, dry grass, etc.) and bare soil. It provides insight into how areas of dry vegetation and/or bare soil and green vegetation are changing over time. The product is derived from Landsat satellite data, using an algorithm developed by the [Joint Remote Sensing Research Program](https://www.jrsrp.org.au/). 4 | Digital Earth Africa's FC service has two components. Fractional Cover is estimated from each Landsat scene, providing measurements from individual days. Fractional Cover Annual Summary (Percentiles) provides 10th, 50th, and 90th percentiles estimated independently for the green vegetation, non-green vegetation, and bare soil fractions observed in each calendar year (1st of January - 31st December). 5 | While the scene based Fractional Cover can be used to study dynamic processes, the annual summaries make it easier to analyse year to year changes. The percentiles provide robust estimates of the low, median and high proportion values observed for each cover type in a year, which can be used to characterise the land cover. 6 | Documentation: https://docs.digitalearthafrica.org/en/latest/data_specs/Fractional_Cover_specs.html 7 | Contact: helpdesk@digitalearthafrica.org 8 | ManagedBy: "[Digital Earth Africa](https://www.digitalearthafrica.org/)" 9 | UpdateFrequency: New scene-level data is added as new Landsat data is available. New summaries are available soon after data is available for a year. 10 | Collabs: 11 | ASDI: 12 | Tags: 13 | - satellite imagery 14 | Tags: 15 | - aws-pds 16 | - agriculture 17 | - disaster response 18 | - earth observation 19 | - geospatial 20 | - natural resource 21 | - satellite imagery 22 | - sustainability 23 | - deafrica 24 | - stac 25 | - cog 26 | License: | 27 | DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/. 28 | Resources: 29 | - Description: Fractional Cover data 30 | ARN: arn:aws:s3:::deafrica-services/fc_ls 31 | Region: af-south-1 32 | Type: S3 Bucket 33 | RequesterPays: False 34 | Explore: 35 | - '[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/fc_ls)' 36 | - Description: Fractional Cover Annual Summary data 37 | ARN: arn:aws:s3:::deafrica-services/fc_ls_summary_annual 38 | Region: af-south-1 39 | Type: S3 Bucket 40 | RequesterPays: False 41 | Explore: 42 | - '[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/fc_ls_summary_annual)' 43 | - Description: "[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents)" 44 | ARN: arn:aws:s3:::deafrica-services-inventory 45 | Region: af-south-1 46 | Type: S3 Bucket 47 | - Description: New scene notifications, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message contains entire STAC record for each new Item 48 | ARN: arn:aws:sns:af-south-1:565417506782:deafrica-landsat-wofs 49 | Region: af-south-1 50 | Type: SNS Topic 51 | - Description: Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent by the s3 bucket for all object create events. 52 | ARN: arn:aws:sns:af-south-1:543785577597:deafrica-services-topic 53 | Region: af-south-1 54 | Type: SNS Topic 55 | DataAtWork: 56 | Tutorials: 57 | - Title: Digital Earth Africa Training 58 | URL: http://learn.digitalearthafrica.org/ 59 | AuthorName: Digital Earth Africa Contributors 60 | Tools & Applications: 61 | - Title: "Digital Earth Africa Explorer (Fractional Cover)" 62 | URL: https://explorer.digitalearth.africa/products/fc_ls 63 | AuthorName: Digital Earth Africa Contributors 64 | - Title: "Digital Earth Africa Explorer (Fractional Cover Annual Summary)" 65 | URL: https://explorer.digitalearth.africa/products/fc_ls_summary_annual 66 | AuthorName: Digital Earth Africa Contributors 67 | - Title: "Digital Earth Africa web services" 68 | URL: https://ows.digitalearth.africa 69 | AuthorName: Digital Earth Africa Contributors 70 | - Title: "Digital Earth Africa Map" 71 | URL: https://maps.digitalearth.africa/ 72 | AuthorName: Digital Earth Africa Contributors 73 | - Title: "Digital Earth Africa Sandbox" 74 | URL: https://sandbox.digitalearth.africa/ 75 | AuthorName: Digital Earth Africa Contributors 76 | - Title: "Digital Earth Africa Notebook Repo" 77 | URL: https://github.com/digitalearthafrica/deafrica-sandbox-notebooks 78 | AuthorName: Digital Earth Africa Contributors 79 | - Title: "Digital Earth Africa Geoportal" 80 | URL: https://www.africageoportal.com/pages/digital-earth-africa 81 | AuthorName: Digital Earth Africa Contributors 82 | Publications: 83 | - Title: "Introduction to DE Africa" 84 | URL: https://youtu.be/Wkf7N6O9jJQ 85 | AuthorName: Dr Fang Yuan 86 | -------------------------------------------------------------------------------- /datasets/deafrica-landsat.yaml: -------------------------------------------------------------------------------- 1 | Name: Digital Earth Africa Landsat Collection 2 Level 2 2 | Description: | 3 | Digital Earth Africa (DE Africa) provides free and open access to a copy of Landsat Collection 2 Level-2 products over Africa. These products are produced and provided by the United States Geological Survey (USGS). 4 | The Landsat series of Earth Observation satellites, jointly led by USGS and NASA, have been continuously acquiring images of the Earth’s land surface since 1972. DE Africa provides data from Landsat 5, 7 and 8 satellites, including historical observations dating back to late 1980s and regularly updated new acquisitions. 5 | New Level-2 Landsat 7 and Landsat 8 data are available after 15 to 27 days from acquisition. See Landsat Collection 2 Generation Timeline for details. 6 | USGS Landsat Collection 2 was released early 2021 and offers improved processing, geometric accuracy, and radiometric calibration compared to previous Collection 1 products. The Level-2 products are endorsed by the Committee on Earth Observation Satellites (CEOS) to be Analysis Ready Data for Land (CARD4L)-compliant. This internationally recognized certification ensures these products have been processed to a minimum set of requirements and organized into a form that allows immediate analysis with a minimum of additional user effort and interoperability both through time and with other datasets. 7 | Documentation: https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_SR_specs.html https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_C2_ST_specs.html 8 | Contact: helpdesk@digitalearthafrica.org 9 | ManagedBy: "[Digital Earth Africa](https://www.digitalearthafrica.org/)" 10 | UpdateFrequency: New Landsat data are added regularly, usually within a few hours of them being available in the usgs-landsat bucket. 11 | Collabs: 12 | ASDI: 13 | Tags: 14 | - satellite imagery 15 | Tags: 16 | - aws-pds 17 | - agriculture 18 | - earth observation 19 | - satellite imagery 20 | - geospatial 21 | - natural resource 22 | - disaster response 23 | - deafrica 24 | - stac 25 | - cog 26 | License: | 27 | There are no restrictions on Landsat data downloaded from the USGS; it can be used or 28 | redistributed as desired. USGS request that you include a [statement of the data source](https://www.usgs.gov/centers/eros/data-citation?qt-science_support_page_related_con=0#qt-science_support_page_related_con) 29 | when citing, copying, or reprinting USGS Landsat data or images. 30 | Resources: 31 | - Description: Landsat scenes and metadata 32 | ARN: arn:aws:s3:::deafrica-landsat 33 | Region: af-south-1 34 | Type: S3 Bucket 35 | RequesterPays: False 36 | Explore: 37 | - '[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/)' 38 | - Description: "[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents) files" 39 | ARN: arn:aws:s3:::deafrica-landsat-inventory 40 | Region: af-south-1 41 | Type: S3 Bucket 42 | - Description: New scene notifications, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message contains entire STAC ST and SR record for each new Item 43 | ARN: arn:aws:sns:af-south-1:543785577597:deafrica-landsat-scene-topic 44 | Region: af-south-1 45 | Type: SNS Topic 46 | - Description: Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent by deafrica-landsat s3 bucket all object create events. 47 | ARN: arn:aws:sns:af-south-1:543785577597:deafrica-landsat-topic 48 | Region: af-south-1 49 | Type: SNS Topic 50 | DataAtWork: 51 | Tutorials: 52 | - Title: Digital Earth Africa Training 53 | URL: http://learn.digitalearthafrica.org/ 54 | AuthorName: Digital Earth Africa Contributors 55 | Tools & Applications: 56 | - Title: "Digital Earth Africa Explorer (LS8 Surface Reflectance)" 57 | URL: https://explorer.digitalearth.africa/products/ls8_sr 58 | AuthorName: Digital Earth Africa Contributors 59 | - Title: "Digital Earth Africa Explorer (LS8 Surface Temperature)" 60 | URL: https://explorer.digitalearth.africa/products/ls8_st 61 | AuthorName: Digital Earth Africa Contributors 62 | - Title: "Digital Earth Africa Explorer (LS7 Surface Reflectance)" 63 | URL: https://explorer.digitalearth.africa/products/ls7_sr 64 | AuthorName: Digital Earth Africa Contributors 65 | - Title: "Digital Earth Africa Explorer (LS7 Surface Temperature)" 66 | URL: https://explorer.digitalearth.africa/products/ls7_st 67 | AuthorName: Digital Earth Africa Contributors 68 | - Title: "Digital Earth Africa Explorer (LS5 Surface Reflectance)" 69 | URL: https://explorer.digitalearth.africa/products/ls5_sr 70 | AuthorName: Digital Earth Africa Contributors 71 | - Title: "Digital Earth Africa Explorer (LS5 Surface Temperature)" 72 | URL: https://explorer.digitalearth.africa/products/ls5_st 73 | AuthorName: Digital Earth Africa Contributors 74 | - Title: "Digital Earth Africa web services" 75 | URL: https://ows.digitalearth.africa 76 | AuthorName: Digital Earth Africa Contributors 77 | - Title: "Digital Earth Africa Map" 78 | URL: https://maps.digitalearth.africa/ 79 | AuthorName: Digital Earth Africa Contributors 80 | - Title: "Digital Earth Africa Sandbox" 81 | URL: https://sandbox.digitalearth.africa/ 82 | AuthorName: Digital Earth Africa Contributors 83 | - Title: "Digital Earth Africa Notebook Repo" 84 | URL: https://github.com/digitalearthafrica/deafrica-sandbox-notebooks 85 | AuthorName: Digital Earth Africa Contributors 86 | - Title: "Digital Earth Africa Geoportal" 87 | URL: https://www.africageoportal.com/pages/digital-earth-africa 88 | AuthorName: ESRI 89 | Publications: 90 | - Title: "Introduction to DE Africa" 91 | URL: https://youtu.be/Wkf7N6O9jJQ 92 | AuthorName: Dr Fang Yuan 93 | -------------------------------------------------------------------------------- /datasets/deafrica-mangrove.yaml: -------------------------------------------------------------------------------- 1 | Name: Digital Earth Africa Global Mangrove Watch 2 | Description: | 3 | The Global Mangrove Watch (GMW) dataset is a result of the collaboration between Aberystwyth University (U.K.), solo Earth Observation (soloEO; Japan), Wetlands International the World Conservation Monitoring Centre (UNEP-WCMC) and the Japan Aerospace Exploration Agency (JAXA). The primary objective of producing this dataset is to provide countries lacking a national mangrove monitoring system with first cut mangrove extent and change maps, to help safeguard against further mangrove forest loss and degradation. 4 | The Global Mangrove Watch dataset (version 2) consists of a global baseline map of mangroves for 2010 and changes from this baseline for six epochs i.e. 1996, 2007, 2008, 2009, 2015 and 2016. Annual maps are planned from 2018 and onwards. The dataset can be used to identify mangrove ecosystems and monitor changes in mangrove extent. This is important in applications such as quantifying ‘blue carbon’, mitigating risks from natural disasters, and prioritising restoration activities. For more information on the Global Watch Mangrove product see the Global Mangrove Watch website. 5 | Documentation: https://docs.digitalearthafrica.org/en/latest/data_specs/Global_Mangrove_Watch_specs.html 6 | Contact: helpdesk@digitalearthafrica.org 7 | ManagedBy: "[Digital Earth Africa](https://www.digitalearthafrica.org/)" 8 | UpdateFrequency: To be defined. 9 | Collabs: 10 | ASDI: 11 | Tags: 12 | - satellite imagery 13 | Tags: 14 | - aws-pds 15 | - natural resource 16 | - earth observation 17 | - coastal 18 | - geospatial 19 | - satellite imagery 20 | - sustainability 21 | - deafrica 22 | - stac 23 | - cog 24 | - land cover 25 | License: | 26 | DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/. 27 | Resources: 28 | - Description: Global Mangrove Watch 29 | ARN: arn:aws:s3:::deafrica-input-datasets/gmw 30 | Region: af-south-1 31 | Type: S3 Bucket 32 | RequesterPays: False 33 | Explore: 34 | - '[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/gmw)' 35 | - Description: "[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents)" 36 | ARN: arn:aws:s3:::deafrica-services-inventory 37 | Region: af-south-1 38 | Type: S3 Bucket 39 | - Description: Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent by the s3 bucket for all object create events. 40 | ARN: arn:aws:sns:af-south-1:543785577597:deafrica-input-datasets-topic 41 | Region: af-south-1 42 | Type: SNS Topic 43 | DataAtWork: 44 | Tutorials: 45 | - Title: Digital Earth Africa Training 46 | URL: http://learn.digitalearthafrica.org/ 47 | AuthorName: Digital Earth Africa Contributors 48 | - Title: Digital Earth Africa Global Mangrove Watch Notebook 49 | URL: https://docs.digitalearthafrica.org/en/latest/sandbox/notebooks/Datasets/Global_Mangrove_Watch.html 50 | AuthorName: Digital Earth Africa Contributors 51 | - Title: Digital Earth Africa Monitoring Mangrove Extents Notebook 52 | URL: https://docs.digitalearthafrica.org/en/latest/sandbox/notebooks/Real_world_examples/Mangrove_analysis.html 53 | AuthorName: Digital Earth Africa Contributors 54 | Tools & Applications: 55 | - Title: "Global Mangrove Watch online platform" 56 | URL: https://www.globalmangrovewatch.org/ 57 | AuthorName: Global Mangrove Alliance 58 | AuthorURL: https://www.mangrovealliance.org/ 59 | - Title: "Digital Earth Africa Explorer (Global Mangrove Watch)" 60 | URL: https://explorer.digitalearth.africa/products/gmw 61 | AuthorName: Digital Earth Africa Contributors 62 | - Title: "Digital Earth Africa web services" 63 | URL: https://ows.digitalearth.africa 64 | AuthorName: Digital Earth Africa Contributors 65 | - Title: "Digital Earth Africa Map" 66 | URL: https://maps.digitalearth.africa/ 67 | AuthorName: Digital Earth Africa Contributors 68 | - Title: "Digital Earth Africa Sandbox" 69 | URL: https://sandbox.digitalearth.africa/ 70 | AuthorName: Digital Earth Africa Contributors 71 | - Title: "Digital Earth Africa Notebook Repo" 72 | URL: https://github.com/digitalearthafrica/deafrica-sandbox-notebooks 73 | AuthorName: Digital Earth Africa Contributors 74 | - Title: "Digital Earth Africa Geoportal" 75 | URL: https://www.africageoportal.com/pages/digital-earth-africa 76 | AuthorName: Digital Earth Africa Contributors 77 | Publications: 78 | - Title: "Time series for nature: Preserving mangroves in Zanzibar" 79 | URL: https://www.digitalearthafrica.org/why-digital-earth-africa/impact-stories/time-series-nature-preserving-mangroves-zanzibar 80 | AuthorName: Digital Earth Africa Contributors 81 | - Title: "Climate Next: How data and community can save Zanzibar’s mangroves" 82 | URL: https://www.aboutamazon.com/news/aws/climate-next-how-data-and-community-can-save-zanzibars-mangroves 83 | AuthorName: Amazon Staff 84 | - Title: "Zanzibar: The Essential Mangrove | Climate Next by AWS" 85 | URL: https://www.youtube.com/watch?v=FVmcEaemfmA 86 | AuthorName: Amazon Web Services (AWS) 87 | 88 | 89 | -------------------------------------------------------------------------------- /datasets/deafrica-ndvi_anomaly.yaml: -------------------------------------------------------------------------------- 1 | Name: Digital Earth Africa Monthly Normalised Difference Vegetation Index (NDVI) Anomaly 2 | Description: | 3 | Digital Earth Africa’s Monthly NDVI Anomaly service provides estimate of vegetation condition, for each caldendar month, against the long-term baseline condition measured for the month from 1984 to 2020 in the NDVI Climatology. 4 | 5 | A standardised anomaly is calculated by subtracting the long-term mean from an observation of interest and then dividing the result by the long-term standard deviation. Positive NDVI anomaly values indicate vegetation is greener than average conditions, and are usually due to increased rainfall in a region. Negative values indicate additional plant stress relative to the long-term average. The NDVI anomaly service is therefore effective for understanding the extent, intensity and impact of a drought.Abrupt and significant negative anomalies may also be caused by fire disturbance. 6 | 7 | Documentation: https://docs.digitalearthafrica.org/en/latest/data_specs/NDVI_Anomaly_specs.html 8 | Contact: helpdesk@digitalearthafrica.org 9 | ManagedBy: "[Digital Earth Africa](https://www.digitalearthafrica.org/)" 10 | UpdateFrequency: From September 2022, the Monthly NDVI Anomaly is generated as a low latency product, i.e. anomaly for a month is generated on the 5th day of the following month. This ensures data is available shortly after the end of a month and all Landat 9 and Sentinel-2 observations are included. Not all landsat 8 observations for the month will be used, because the Landsat 8 Surface Refelectance product from USGS has a latency of over 2 weeks ([see Landsat Collection 2 Generation Timeline (https://www.usgs.gov/media/images/landsat-collection-2-generation-timeline)). 11 | 12 | Collabs: 13 | ASDI: 14 | Tags: 15 | - satellite imagery 16 | Tags: 17 | - aws-pds 18 | - agriculture 19 | - disaster response 20 | - earth observation 21 | - geospatial 22 | - natural resource 23 | - satellite imagery 24 | - deafrica 25 | - stac 26 | - cog 27 | License: | 28 | DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/. 29 | Resources: 30 | - Description: Monthly Normalised Difference Vegetation Index (NDVI) Anomaly 31 | ARN: arn:aws:s3:::deafrica-services/ndvi_anomaly 32 | Region: af-south-1 33 | Type: S3 Bucket 34 | RequesterPays: False 35 | Explore: 36 | - '[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/ndvi_anomaly)' 37 | - Description: "[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents)" 38 | ARN: arn:aws:s3:::deafrica-services-inventory 39 | Region: af-south-1 40 | Type: S3 Bucket 41 | - Description: New scene notifications, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message contains entire STAC record for each new Item 42 | ARN: arn:aws:sns:af-south-1:565417506782:deafrica-landsat-wofs 43 | Region: af-south-1 44 | Type: SNS Topic 45 | - Description: Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent by the s3 bucket for all object create events. 46 | ARN: arn:aws:sns:af-south-1:543785577597:deafrica-services-topic 47 | Region: af-south-1 48 | Type: SNS Topic 49 | DataAtWork: 50 | Tutorials: 51 | - Title: Digital Earth Africa Training 52 | URL: http://learn.digitalearthafrica.org/ 53 | AuthorName: Digital Earth Africa Contributors 54 | Tools & Applications: 55 | - Title: "Digital Earth Africa Explorer (Monthly Normalised Difference Vegetation Index (NDVI) Anomaly)" 56 | URL: https://explorer.digitalearth.africa/products/ndvi_anomaly 57 | AuthorName: Digital Earth Africa Contributors 58 | - Title: "Digital Earth Africa web services" 59 | URL: https://ows.digitalearth.africa 60 | AuthorName: Digital Earth Africa Contributors 61 | - Title: "Digital Earth Africa Map" 62 | URL: https://maps.digitalearth.africa/ 63 | AuthorName: Digital Earth Africa Contributors 64 | - Title: "Digital Earth Africa Sandbox" 65 | URL: https://sandbox.digitalearth.africa/ 66 | AuthorName: Digital Earth Africa Contributors 67 | - Title: "Digital Earth Africa Notebook Repo" 68 | URL: https://github.com/digitalearthafrica/deafrica-sandbox-notebooks 69 | AuthorName: Digital Earth Africa Contributors 70 | - Title: "Digital Earth Africa Geoportal" 71 | URL: https://www.africageoportal.com/pages/digital-earth-africa 72 | AuthorName: Digital Earth Africa Contributors 73 | 74 | Publications: 75 | - Title: "Digital Earth Africa releases new Rolling Monthly GeoMAD continental service" 76 | URL: https://www.digitalearthafrica.org/media-center/blog/digital-earth-africa-releases-new-rolling-monthly-geomad-continental-service 77 | AuthorName: Digital Earth Africa Contributors 78 | - Title: "Mean NDVI and Anomalies" 79 | URL: https://www.digitalearthafrica.org/platform-resources/services/mean-ndvi-and-anomalies 80 | AuthorName: Digital Earth Africa Contributors 81 | -------------------------------------------------------------------------------- /datasets/deafrica-ndvi_climatology_ls.yaml: -------------------------------------------------------------------------------- 1 | Name: Digital Earth Africa Normalised Difference Vegetation Index (NDVI) Climatology 2 | Description: | 3 | Digital Earth Africa’s NDVI climatology product represents the long-term average baseline condition of vegetation for every Landsat pixel over the African continent. Both mean and standard deviation NDVI climatologies are available for each calender month. 4 | 5 | Some key features of the product are: 6 | 7 | - NDVI climatologies were developed using harmonized Landsat 5,7,and 8 satellite imagery. 8 | - Mean and standard deviation NDVI climatologies are produced for each calender month, using a temporal baseline period from 1984-2020 (inclusive) 9 | - Datasets have a spatial resolution of 30 metres 10 | 11 | 12 | Documentation: https://docs.digitalearthafrica.org/en/latest/data_specs/ndvi_climatology_ls.html 13 | Contact: helpdesk@digitalearthafrica.org 14 | ManagedBy: "[Digital Earth Africa](https://www.digitalearthafrica.org/)" 15 | UpdateFrequency: N/A. 16 | 17 | 18 | Collabs: 19 | ASDI: 20 | Tags: 21 | - satellite imagery 22 | Tags: 23 | - aws-pds 24 | - agriculture 25 | - disaster response 26 | - earth observation 27 | - geospatial 28 | - natural resource 29 | - agriculture 30 | - satellite imagery 31 | - deafrica 32 | - stac 33 | - cog 34 | License: | 35 | DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/. 36 | Resources: 37 | - Description: Normalised Difference Vegetation Index (NDVI) Climatology 38 | ARN: arn:aws:s3:::deafrica-services/ndvi_climatology_ls 39 | Region: af-south-1 40 | Type: S3 Bucket 41 | RequesterPays: False 42 | Explore: 43 | - '[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/ndvi_climatology_ls)' 44 | - Description: "[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents)" 45 | ARN: arn:aws:s3:::deafrica-services-inventory 46 | Region: af-south-1 47 | Type: S3 Bucket 48 | - Description: New scene notifications, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message contains entire STAC record for each new Item 49 | ARN: arn:aws:sns:af-south-1:565417506782:deafrica-landsat-wofs 50 | Region: af-south-1 51 | Type: SNS Topic 52 | - Description: Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent by the s3 bucket for all object create events. 53 | ARN: arn:aws:sns:af-south-1:543785577597:deafrica-services-topic 54 | Region: af-south-1 55 | Type: SNS Topic 56 | DataAtWork: 57 | Tutorials: 58 | - Title: Digital Earth Africa Training 59 | URL: http://learn.digitalearthafrica.org/ 60 | AuthorName: Digital Earth Africa Contributors 61 | Tools & Applications: 62 | - Title: "Digital Earth Africa Explorer(Normalised Difference Vegetation Index (NDVI) Climatology)" 63 | URL: https://explorer.digitalearth.africa/products/ndvi_climatology_ls 64 | AuthorName: Digital Earth Africa Contributors 65 | - Title: "Digital Earth Africa web services" 66 | URL: https://ows.digitalearth.africa 67 | AuthorName: Digital Earth Africa Contributors 68 | - Title: "Digital Earth Africa Map" 69 | URL: https://maps.digitalearth.africa/ 70 | AuthorName: Digital Earth Africa Contributors 71 | - Title: "Digital Earth Africa Sandbox" 72 | URL: https://sandbox.digitalearth.africa/ 73 | AuthorName: Digital Earth Africa Contributors 74 | - Title: "Digital Earth Africa Notebook Repo" 75 | URL: https://github.com/digitalearthafrica/deafrica-sandbox-notebooks 76 | AuthorName: Digital Earth Africa Contributors 77 | - Title: "Digital Earth Africa Geoportal" 78 | URL: https://www.africageoportal.com/pages/digital-earth-africa 79 | AuthorName: Digital Earth Africa Contributors 80 | 81 | Publications: 82 | - Title: "Mean NDVI and Anomalies" 83 | URL: https://www.digitalearthafrica.org/platform-resources/services/mean-ndvi-and-anomalies 84 | AuthorName: Digital Earth Africa Contributors 85 | 86 | 87 | 88 | -------------------------------------------------------------------------------- /datasets/deafrica-sentinel-1.yaml: -------------------------------------------------------------------------------- 1 | Name: Digital Earth Africa Sentinel-1 Radiometrically Terrain Corrected 2 | Description: | 3 | DE Africa’s Sentinel-1 backscatter product is developed to be compliant with the CEOS Analysis Ready Data for Land (CARD4L) specifications. 4 | The Sentinel-1 mission, composed of a constellation of two C-band Synthetic Aperture Radar (SAR) satellites, are operated by European Space Agency (ESA) as part of the Copernicus Programme. The mission currently collects data every 12 days over Africa at a spatial resolution of approximately 20 m. 5 | Radar backscatter measures the amount of microwave radiation reflected back to the sensor from the ground surface. This measurement is sensitive to surface roughness, moisture content and viewing geometry. DE Africa provides Sentinel-1 backscatter as Radiometrically Terrain Corrected (RTC) gamma-0 (γ0) where variation due to changing observation geometries has been mitigated. 6 | The dual polarisation backcastter time series can be used in applications for forests, agriculture, wetlands and land cover classification. SAR’s ability to ‘see through’ clouds makes it critical for mapping and monitoring land cover changes in the wet tropics. 7 | Documentation: https://docs.digitalearthafrica.org/en/latest/data_specs/Sentinel-1_specs.html 8 | Contact: helpdesk@digitalearthafrica.org 9 | ManagedBy: "[Digital Earth Africa](https://www.digitalearthafrica.org/)" 10 | UpdateFrequency: New Sentinel-1 data are added regularly. 11 | Collabs: 12 | ASDI: 13 | Tags: 14 | - satellite imagery 15 | Tags: 16 | - aws-pds 17 | - agriculture 18 | - earth observation 19 | - satellite imagery 20 | - geospatial 21 | - natural resource 22 | - disaster response 23 | - deafrica 24 | - stac 25 | - cog 26 | - synthetic aperture radar 27 | License: | 28 | Access to Sentinel data is free, full and open for the broad Regional, National, European and International user community. View [Terms and Conditions](https://scihub.copernicus.eu/twiki/do/view/SciHubWebPortal/TermsConditions). 29 | Resources: 30 | - Description: Sentinel-1 tiles and metadata 31 | ARN: arn:aws:s3:::deafrica-sentinel-1 32 | Region: af-south-1 33 | Type: S3 Bucket 34 | RequesterPays: False 35 | Explore: 36 | - '[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/s1_rtc)' 37 | - Description: "[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents)" 38 | ARN: arn:aws:s3:::deafrica-sentinel-1-inventory 39 | Region: af-south-1 40 | Type: S3 Bucket 41 | - Description: New scene notifications, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message contains entire STAC record for each new Item. 42 | ARN: arn:aws:sns:af-south-1:543785577597:deafrica-sentinel-1-scene-topic 43 | Region: af-south-1 44 | Type: SNS Topic 45 | - Description: Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent by deafrica-sentinel-1 s3 bucket all object create events. 46 | ARN: arn:aws:sns:af-south-1:543785577597:deafrica-sentinel-1-topic 47 | Region: af-south-1 48 | Type: SNS Topic 49 | DataAtWork: 50 | Tutorials: 51 | - Title: Digital Earth Africa Training 52 | URL: http://learn.digitalearthafrica.org/ 53 | AuthorName: Digital Earth Africa Contributors 54 | - Title: Water detection with Sentinel-1 55 | URL: https://docs.digitalearthafrica.org/en/latest/sandbox/notebooks/Real_world_examples/Radar_water_detection.html 56 | NotebookURL: https://github.com/mseehaber/dea_sagemaker/blob/master/s1rtc-load-analysis.ipynb 57 | AuthorName: Madeleine Seehaber 58 | Services: 59 | - Amazon SageMaker Studio Lab 60 | Tools & Applications: 61 | - Title: "Digital Earth Africa Explorer" 62 | URL: https://explorer.digitalearth.africa/products/s1_rtc/extents 63 | AuthorName: Digital Earth Africa Contributors 64 | - Title: "Digital Earth Africa web services" 65 | URL: https://ows.digitalearth.africa 66 | AuthorName: Digital Earth Africa Contributors 67 | - Title: "Digital Earth Africa Map" 68 | URL: https://maps.digitalearth.africa/ 69 | AuthorName: Digital Earth Africa Contributors 70 | - Title: "Digital Earth Africa Sandbox" 71 | URL: https://sandbox.digitalearth.africa/ 72 | AuthorName: Digital Earth Africa Contributors 73 | - Title: "Digital Earth Africa Notebook Repo" 74 | URL: https://github.com/digitalearthafrica/deafrica-sandbox-notebooks 75 | AuthorName: Digital Earth Africa Contributors 76 | - Title: "Digital Earth Africa Geoportal" 77 | URL: https://www.africageoportal.com/pages/digital-earth-africa 78 | AuthorName: Digital Earth Africa Contributors 79 | Publications: 80 | - Title: "Introduction to DE Africa" 81 | URL: https://youtu.be/Wkf7N6O9jJQ 82 | AuthorName: Dr Fang Yuan 83 | - Title: "An Operational Analysis Ready Radar Backscatter Dataset for the African Continent" 84 | URL: https://doi.org/10.3390/rs14020351 85 | AuthorName: Fang Yuan, Marko Repse, Alex Leith, Ake Rosenqvist, Grega Milcinski, Negin F. Moghaddam, Tishampati Dhar, Chad Burton, Lisa Hall, Cedric Jorand and Adam Lewis 86 | -------------------------------------------------------------------------------- /datasets/deafrica-sentinel-2.yaml: -------------------------------------------------------------------------------- 1 | Name: Digital Earth Africa Sentinel-2 Level-2A 2 | Description: | 3 | The Sentinel-2 mission is part of the European Union Copernicus programme for Earth observations. Sentinel-2 consists of twin satellites, Sentinel-2A (launched 23 June 2015) and Sentinel-2B (launched 7 March 2017). The two satellites have the same orbit, but 180° apart for optimal coverage and data delivery. Their combined data is used in the Digital Earth Africa Sentinel-2 product. 4 | Together, they cover all Earth’s land surfaces, large islands, inland and coastal waters every 3-5 days. 5 | Sentinel-2 data is tiered by level of pre-processing. Level-0, Level-1A and Level-1B data contain raw data from the satellites, with little to no pre-processing. Level-1C data is surface reflectance measured at the top of the atmosphere. This is processed using the Sen2Cor algorithm to give Level-2A, the bottom-of-atmosphere reflectance (Obregón et al, 2019). Level-2A data is the most ideal for research activities as it allows further analysis without applying additional atmospheric corrections. 6 | The Digital Earth Africa Sentinel-2 dataset contains Level-2A data of the African continent. Digital Earth Africa does not host any lower-level Sentinel-2 data. 7 | Note that this data is a subset of the Sentinel-2 COGs dataset. 8 | Documentation: https://docs.digitalearthafrica.org/en/latest/data_specs/Sentinel-2_Level-2A_specs.html 9 | Contact: helpdesk@digitalearthafrica.org 10 | ManagedBy: "[Digital Earth Africa](https://www.digitalearthafrica.org/)" 11 | UpdateFrequency: New Sentinel-2 scenes are added regularly, usually within few hours after they are available on Copernicus OpenHub. 12 | Collabs: 13 | ASDI: 14 | Tags: 15 | - satellite imagery 16 | Tags: 17 | - aws-pds 18 | - agriculture 19 | - earth observation 20 | - satellite imagery 21 | - geospatial 22 | - natural resource 23 | - disaster response 24 | - deafrica 25 | - stac 26 | - cog 27 | License: | 28 | Access to Sentinel data is free, full and open for the broad Regional, National, European and International user community. View [Terms and Conditions](https://scihub.copernicus.eu/twiki/do/view/SciHubWebPortal/TermsConditions). 29 | Resources: 30 | - Description: Sentinel-2 scenes and metadata 31 | ARN: arn:aws:s3:::deafrica-sentinel-2 32 | Region: af-south-1 33 | Type: S3 Bucket 34 | RequesterPays: False 35 | Explore: 36 | - '[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/s2_l2a)' 37 | - Description: "[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents)" 38 | ARN: arn:aws:s3:::deafrica-sentinel-2-inventory 39 | Region: af-south-1 40 | Type: S3 Bucket 41 | - Description: New scene notifications, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message contains entire STAC record for each new Item. 42 | ARN: arn:aws:sns:af-south-1:543785577597:deafrica-sentinel-2-scene-topic 43 | Region: af-south-1 44 | Type: SNS Topic 45 | - Description: Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent by deafrica-sentinel-2 s3 bucket all object create events. 46 | ARN: arn:aws:sns:af-south-1:543785577597:deafrica-sentinel-2-topic 47 | Region: af-south-1 48 | Type: SNS Topic 49 | DataAtWork: 50 | Tutorials: 51 | - Title: Use Sentinel-2 data in the Open Data Cube 52 | URL: https://github.com/opendatacube/cube-in-a-box 53 | AuthorName: Alex Leith 54 | - Title: Digital Earth Africa Training 55 | URL: http://learn.digitalearthafrica.org/ 56 | AuthorName: Digital Earth Africa Contributors 57 | - Title: Downloading and streaming data using STAC metadata 58 | URL: https://docs.digitalearthafrica.org/en/latest/sandbox/notebooks/Frequently_used_code/Downloading_data_with_STAC.html 59 | AuthorName: Digital Earth Africa Contributors 60 | Tools & Applications: 61 | - Title: "Digital Earth Africa Explorer" 62 | URL: https://explorer.digitalearth.africa/products/s2_l2a/extents 63 | AuthorName: Digital Earth Africa Contributors 64 | - Title: "Digital Earth Africa web services" 65 | URL: https://ows.digitalearth.africa 66 | AuthorName: Digital Earth Africa Contributors 67 | - Title: "Digital Earth Africa Map" 68 | URL: https://maps.digitalearth.africa/ 69 | AuthorName: Digital Earth Africa Contributors 70 | - Title: "Digital Earth Africa Sandbox" 71 | URL: https://sandbox.digitalearth.africa/ 72 | AuthorName: Digital Earth Africa Contributors 73 | - Title: "Digital Earth Africa Notebook Repo" 74 | URL: https://github.com/digitalearthafrica/deafrica-sandbox-notebooks 75 | AuthorName: Digital Earth Africa Contributors 76 | - Title: "Digital Earth Africa Geoportal" 77 | URL: https://www.africageoportal.com/pages/digital-earth-africa 78 | AuthorName: Digital Earth Africa Contributors 79 | Publications: 80 | - Title: "Introduction to DE Africa" 81 | URL: https://youtu.be/Wkf7N6O9jJQ 82 | AuthorName: Dr Fang Yuan 83 | -------------------------------------------------------------------------------- /datasets/deafrica-waterbodies.yaml: -------------------------------------------------------------------------------- 1 | Name: DE Africa Waterbodies Monitoring Service 2 | Description: | 3 | The Digital Earth Africa continental Waterbodies Monitoring Service identifies more than 700,000 water bodies from over three decades of satellite observations. This service maps persistent and seasonal water bodies and the change in their water surface area over time. Mapped water bodies may include, but are not limited to, lakes, ponds, man-made reservoirs, wetlands, and segments of some river systems.On a local, regional, and continental scale, this service helps improve our understanding of surface water dynamics and water availability and can be used for monitoring water bodies such as wetlands, lakes and dams in remote and/or inaccessible locations. 4 | 5 | Documentation: https://docs.digitalearthafrica.org/en/latest/data_specs/Waterbodies_specs.html 6 | Contact: helpdesk@digitalearthafrica.org 7 | ManagedBy: "[Digital Earth Africa](https://www.digitalearthafrica.org/)" 8 | UpdateFrequency: Single historical extent derived from the full temporal range. 9 | Collabs: 10 | ASDI: 11 | Tags: 12 | - satellite imagery 13 | Tags: 14 | - aws-pds 15 | - agriculture 16 | - disaster response 17 | - earth observation 18 | - geospatial 19 | - natural resource 20 | - satellite imagery 21 | - water 22 | - deafrica 23 | - stac 24 | - cog 25 | License: | 26 | DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/. 27 | Resources: 28 | - Description: DE Africa Waterbodies 29 | ARN: arn:aws:s3:::deafrica-services/waterbodies 30 | Region: af-south-1 31 | Type: S3 Bucket 32 | RequesterPays: False 33 | Explore: 34 | - '[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/waterbodies)' 35 | DataAtWork: 36 | Tutorials: 37 | - Title: "Digital Earth Africa Training" 38 | URL: http://learn.digitalearthafrica.org 39 | AuthorName: Digital Earth Africa Contributors 40 | Tools & Applications: 41 | - Title: "Digital Earth Africa Map" 42 | URL: https://maps.digitalearth.africa 43 | AuthorName: Digital Earth Africa Contributors 44 | - Title: "Digital Earth Africa Sandbox" 45 | URL: https://sandbox.digitalearth.africa 46 | AuthorName: Digital Earth Africa Contributors 47 | - Title: "Digital Earth Africa Notebook Repo" 48 | URL: https://github.com/digitalearthafrica/deafrica-sandbox-notebooks 49 | AuthorName: Digital Earth Africa Contributors 50 | - Title: "Digital Earth Africa Geoportal" 51 | URL: https://www.africageoportal.com/pages/digital-earth-africa 52 | AuthorName: Digital Earth Africa Contributors 53 | Publications: 54 | - Title: "Waterbodies Monitoring Service" 55 | URL: https://www.digitalearthafrica.org/node/601 56 | AuthorName: Digital Earth Africa Contributors 57 | -------------------------------------------------------------------------------- /datasets/deafrica-wofs.yaml: -------------------------------------------------------------------------------- 1 | Name: Digital Earth Africa Water Observations from Space 2 | Description: | 3 | Water Observations from Space (WOfS) is a service that draws on satellite imagery to provide historical surface water observations of the whole African continent. WOfS allows users to understand the location and movement of inland and coastal water present in the African landscape. It shows where water is usually present; where it is seldom observed; and where inundation of the surface has been observed by satellite. 4 | They are generated using the WOfS classification algorithm on Landsat satellite data. There are several WOfS products available for the African continent including scene-level data and annual or all time summaries. 5 | Documentation: https://docs.digitalearthafrica.org/en/latest/data_specs/Landsat_WOfS_specs.html 6 | Contact: helpdesk@digitalearthafrica.org 7 | ManagedBy: "[Digital Earth Africa](https://www.digitalearthafrica.org/)" 8 | UpdateFrequency: New scene-level data is added as new Landsat data is available. New summaries are available soon after data is available for a year. 9 | Collabs: 10 | ASDI: 11 | Tags: 12 | - satellite imagery 13 | Tags: 14 | - aws-pds 15 | - agriculture 16 | - disaster response 17 | - earth observation 18 | - geospatial 19 | - natural resource 20 | - satellite imagery 21 | - water 22 | - deafrica 23 | - stac 24 | - cog 25 | License: | 26 | DE Africa makes this data available under the Creative Commons Attribute 4.0 license https://creativecommons.org/licenses/by/4.0/. 27 | Resources: 28 | - Description: Water Observations from Space data 29 | ARN: arn:aws:s3:::deafrica-services/wofs_ls 30 | Region: af-south-1 31 | Type: S3 Bucket 32 | RequesterPays: False 33 | Explore: 34 | - '[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/wofs_ls)' 35 | - Description: Water Observations from Space Annual Summary data 36 | ARN: arn:aws:s3:::deafrica-services/wofs_ls_summary_annual 37 | Region: af-south-1 38 | Type: S3 Bucket 39 | RequesterPays: False 40 | Explore: 41 | - '[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/wofs_ls_summary_annual)' 42 | - Description: Water Observations from Space All-Time Summary data 43 | ARN: arn:aws:s3:::deafrica-services/wofs_ls_summary_alltime 44 | Region: af-south-1 45 | Type: S3 Bucket 46 | RequesterPays: False 47 | Explore: 48 | - '[STAC V1.0.0 endpoint](https://explorer.digitalearth.africa/stac/collections/wofs_ls_summary_alltime)' 49 | - Description: "[S3 Inventory](https://docs.aws.amazon.com/AmazonS3/latest/dev/storage-inventory.html#storage-inventory-contents)" 50 | ARN: arn:aws:s3:::deafrica-services-inventory 51 | Region: af-south-1 52 | Type: S3 Bucket 53 | - Description: New scene notifications, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message contains entire STAC record for each new Item 54 | ARN: arn:aws:sns:af-south-1:565417506782:deafrica-landsat-wofs 55 | Region: af-south-1 56 | Type: SNS Topic 57 | - Description: Bucket creation event notification, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message sent by the s3 bucket for all object create events. 58 | ARN: arn:aws:sns:af-south-1:543785577597:deafrica-services-topic 59 | Region: af-south-1 60 | Type: SNS Topic 61 | DataAtWork: 62 | Tutorials: 63 | - Title: Digital Earth Africa Training 64 | URL: http://learn.digitalearthafrica.org/ 65 | AuthorName: Digital Earth Africa Contributors 66 | Tools & Applications: 67 | - Title: "Digital Earth Africa Explorer (Water Observations from Space)" 68 | URL: https://explorer.digitalearth.africa/products/wofs_ls 69 | AuthorName: Digital Earth Africa Contributors 70 | - Title: "Digital Earth Africa Explorer (Water Observations from Space Annual Summary)" 71 | URL: https://explorer.digitalearth.africa/products/wofs_ls_summary_annual 72 | AuthorName: Digital Earth Africa Contributors 73 | - Title: "Digital Earth Africa Explorer (Water Observations from Space All-Time Summary)" 74 | URL: https://explorer.digitalearth.africa/products/wofs_ls_summary_alltime 75 | AuthorName: Digital Earth Africa Contributors 76 | - Title: "Digital Earth Africa web services" 77 | URL: https://ows.digitalearth.africa 78 | AuthorName: Digital Earth Africa Contributors 79 | - Title: "Digital Earth Africa Map" 80 | URL: https://maps.digitalearth.africa/ 81 | AuthorName: Digital Earth Africa Contributors 82 | - Title: "Digital Earth Africa Sandbox" 83 | URL: https://sandbox.digitalearth.africa/ 84 | AuthorName: Digital Earth Africa Contributors 85 | - Title: "Digital Earth Africa Notebook Repo" 86 | URL: https://github.com/digitalearthafrica/deafrica-sandbox-notebooks 87 | AuthorName: Digital Earth Africa Contributors 88 | - Title: "Digital Earth Africa Geoportal" 89 | URL: https://www.africageoportal.com/pages/digital-earth-africa 90 | AuthorName: Digital Earth Africa Contributors 91 | Publications: 92 | - Title: "Water Observations from Space: accurate maps of surface water through time for the continent of Africa" 93 | URL: https://agu2021fallmeeting-agu.ipostersessions.com/Default.aspx?s=E9-AF-34-FE-69-38-18-49-15-E0-0F-89-21-74-C0-29 94 | AuthorName: Meghan Halabisky, Kenneth Mubea, Fatou Mar, Fang Yuan, Chad Burton, Eloise Birchall, Negin F. Moghaddam, Sena Ghislain Adimou, Bako Mamane, David Ongo, Edward Boamah, Ee-Faye Chong, Nikita Gandhi, Alex Leith, Lisa Hall and Adam Lewis 95 | - Title: "Analysing effects of drought on inundation extent and vegetation cover dynamics in the Okavango Delta" 96 | URL: https://agu2021fallmeeting-agu.ipostersessions.com/?s=7A-85-3A-E6-1F-5F-19-87-39-23-90-F1-DC-57-5C-E9 97 | AuthorName: Kelebogile Mfundisi, Kenneth Mubea, Fang Yuan, Chad Burton and Edward Boamah 98 | -------------------------------------------------------------------------------- /datasets/esa-worldcover-vito-composites.yaml: -------------------------------------------------------------------------------- 1 | Name: ESA WorldCover Sentinel-1 and Sentinel-2 10m Annual Composites 2 | Description: The WorldCover 10m Annual Composites were produced, as part of the European Space Agency (ESA) WorldCover project, from the yearly Copernicus Sentinel-1 and Sentinel-2 archives for both years 2020 and 2021. These global mosaics consists of four products composites. A Sentinel-2 RGBNIR yearly median composite for bands B02, B03, B04, B08. A Sentinel-2 SWIR yearly median composite for bands B11 and B12. A Sentinel-2 NDVI yearly percentiles composite (NDVI 90th, NDVI 50th NDVI 10th percentiles). A Sentinel-1 GAMMA0 yearly median composite for bands VV, VH and VH/VV (power scaled). Each product is delivered as a series of Cloud-Optimized GeoTIFFs (COGs) in WSG84 projection in a grid of 1 by 1 degrees and at 0.3 arc seconds resolution (approx. 10m), except for the SWIR composite which is delivered at 0.6 arc seconds (approx. 20m). The Sentinel-2 composites were produced from the L2A archive. The GAMMA0 composite was produced by pre-processing the Sentinel-1 GRD products using [GAMMA software](https://www.gamma-rs.ch/). 3 | Documentation: More information is available on the products' [GitHub](https://github.com/ESA-WorldCover/esa-worldcover-datasets) and on WorldCover project [website](https://esa-worldcover.org/en/data-access). 4 | Contact: https://esa-worldcover.org/en/contact 5 | ManagedBy: "[VITO](https://vito.be)" 6 | UpdateFrequency: Not updated. 7 | Collabs: 8 | ASDI: 9 | Tags: 10 | - satellite imagery 11 | Tags: 12 | - aws-pds 13 | - earth observation 14 | - agriculture 15 | - satellite imagery 16 | - geospatial 17 | - natural resource 18 | - sustainability 19 | - cog 20 | - disaster response 21 | - mapping 22 | - synthetic aperture radar 23 | - land cover 24 | - land use 25 | - machine learning 26 | - stac 27 | License: "CC-BY 4.0" 28 | Citation: "To cite the dataset in a publication please refer to the [Citation section](https://esa-worldcover.org/en/data-access)/[DOI v200](https://doi.org/10.5281/zenodo.7254221)/[DOI v100](https://doi.org/10.5281/zenodo.5571936)." 29 | Resources: 30 | - Description: ESA WorldCover S2 composites. The bucket contains the 3 Sentinel-2 L2A annual composites for the years 2020 and 2021. A 4 bands RGBNIR yearly median composite (B02, B03, B04, B08), a 2 bands SWIR yearly median composite (B11, B12) and a 3 bands NDVI yearly percentiles composite (NDVI p90, NDVI p50, NDVI p10). 31 | ARN: arn:aws:s3:::esa-worldcover-s2 32 | Region: eu-central-1 33 | Type: S3 Bucket 34 | RequesterPays: False 35 | Explore: 36 | - '[Products Grid](https://esa-worldcover.s3.eu-central-1.amazonaws.com/esa_worldcover_grid_composites.fgb)' 37 | - '[WorldCover Viewer](https://viewer.esa-worldcover.org/worldcover/?layer=WORLDCOVER_2021_S2_NDVI)' 38 | - Description: ESA WorldCover S1 composites. The bucket contains 1 Sentinel-1 annual composite for the years 2020 and 2021. A 3 bands GAMMA0 yearly median composite (VV, VH, VH/VV), power scaled. 39 | ARN: arn:aws:s3:::esa-worldcover-s1 40 | Region: eu-central-1 41 | Type: S3 Bucket 42 | RequesterPays: False 43 | Explore: 44 | - '[Products Grid](https://esa-worldcover.s3.eu-central-1.amazonaws.com/esa_worldcover_grid_composites.fgb)' 45 | - '[WorldCover Viewer](https://viewer.esa-worldcover.org/worldcover/?layer=WORLDCOVER_2021_S1_VVVHratio)' 46 | DataAtWork: 47 | Tutorials: 48 | - Title: Exploring the datasets 49 | URL: https://github.com/ESA-WorldCover/esa-worldcover-datasets 50 | AuthorName: VITO 51 | AuthorURL: https://esa-worldcover.org 52 | Tools & Applications: 53 | - Title: WorldCover Viewer 54 | URL: https://viewer.esa-worldcover.org/worldcover/ 55 | AuthorName: VITO 56 | AuthorURL: https://esa-worldcover.org/en 57 | Publications: 58 | - Title: ESA WorldCover 10 m 2021 v200 59 | URL: https://doi.org/10.5281/zenodo.7254221 60 | AuthorName: Zanaga, D., Van De Kerchove, R.,Daems, D.,De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., Fritz, S., Lesiv, M., Herold, M., Tsendbazar, N.E., Xu, P., Ramoino, F., Arino, O. 61 | - Title: Release of the 10 m WorldCover map 62 | URL: https://blog.vito.be/remotesensing/release-of-the-10-m-worldcover-map 63 | AuthorName: Ruben Van De Kerchove 64 | - Title: ESA WorldCover 10 m 2021 v200 - Product User Manual 65 | URL: https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PUM_V2.0.pdf 66 | AuthorName: VITO 67 | 68 | 69 | -------------------------------------------------------------------------------- /datasets/esa-worldcover-vito.yaml: -------------------------------------------------------------------------------- 1 | Name: ESA WorldCover 2 | Description: The European Space Agency (ESA) WorldCover product provides global land cover maps for 2020 & 2021 at 10 m resolution based on Copernicus Sentinel-1 and Sentinel-2 data. The WorldCover product comes with 11 land cover classes and has been generated in the framework of the ESA WorldCover project, part of the 5th Earth Observation Envelope Programme (EOEP-5) of the European Space Agency. A first version of the product (v100), containing the 2020 map was released in October 2021. The 2021 map was released in October 2022 using an improved algorithm (v200). The WorldCover 2020 and 2021 maps were generated with different algorithm versions and therefore changes between the maps should be treated with caution as these contain both real land cover changes as well as changes due to the used algorithms. 3 | 4 | Documentation: Documentation is available [here](https://esa-worldcover.org/en/data-access). 5 | Contact: https://esa-worldcover.org/en/contact 6 | ManagedBy: "[VITO](https://vito.be)" 7 | UpdateFrequency: Yearly. 8 | Collabs: 9 | ASDI: 10 | Tags: 11 | - satellite imagery 12 | Tags: 13 | - aws-pds 14 | - earth observation 15 | - agriculture 16 | - satellite imagery 17 | - geospatial 18 | - natural resource 19 | - sustainability 20 | - cog 21 | - disaster response 22 | - mapping 23 | - synthetic aperture radar 24 | - land cover 25 | - land use 26 | - machine learning 27 | - stac 28 | License: "CC-BY 4.0" 29 | Citation: "To cite the dataset in a publication please refer to the [Citation section](https://esa-worldcover.org/en/data-access)/[DOI v200](https://doi.org/10.5281/zenodo.7254221)/[DOI v100](https://doi.org/10.5281/zenodo.5571936)." 30 | Resources: 31 | - Description: ESA WorldCover in a S3 bucket 32 | ARN: arn:aws:s3:::esa-worldcover 33 | Region: eu-central-1 34 | Type: S3 Bucket 35 | RequesterPays: False 36 | Explore: 37 | - '[STAC endpoint](https://services.terrascope.be/stac/)' 38 | DataAtWork: 39 | Tutorials: 40 | - Title: Exploring the dataset (STAC API examples) 41 | URL: https://github.com/ESA-WorldCover/esa-worldcover-datasets 42 | AuthorName: VITO 43 | AuthorURL: https://esa-worldcover.org 44 | - Title: Global Fire Spread Prediction System 45 | URL: https://github.com/SatelliteVu/SatelliteVu-AWS-Disaster-Response-Hackathon 46 | AuthorName: SatelliteVu 47 | AuthorURL: https://www.satellitevu.com/ 48 | Tools & Applications: 49 | - Title: WorldCover Viewer 50 | URL: https://viewer.esa-worldcover.org/worldcover/ 51 | AuthorName: VITO 52 | AuthorURL: https://esa-worldcover.org/en 53 | - Title: TerraScope Viewer 54 | URL: https://viewer.terrascope.be/ 55 | AuthorName: TerraScope 56 | AuthorURL: https://terrascope.be/en/about-us 57 | - Title: ESA Viewer 2020 58 | URL: https://worldcover2020.esa.int/viewer 59 | AuthorName: ESA 60 | AuthorURL: https://worldcover2020.esa.int/ 61 | - Title: ESA Viewer 2021 62 | URL: https://worldcover2021.esa.int/viewer 63 | AuthorName: ESA 64 | AuthorURL: https://worldcover2021.esa.int/ 65 | - Title: WorldCover GEE App 66 | URL: https://vitorsveg.users.earthengine.app/view/worldcover 67 | AuthorName: VITO 68 | AuthorURL: https://esa-worldcover.org 69 | Publications: 70 | - Title: ESA WorldCover 10 m 2020 v100 71 | URL: https://doi.org/10.5281/zenodo.5571936 72 | AuthorName: Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., Vergnaud, S., Cartus, O., Santoro, M., Fritz, S., Georgieva, I., Lesiv, M., Carter, S., Herold, M., Li, Linlin, Tsendbazar, N.E., Ramoino, F., Arino, O., 2021. 73 | - Title: ESA WorldCover 10 m 2021 v200 74 | URL: https://doi.org/10.5281/zenodo.7254221 75 | AuthorName: Zanaga, D., Van De Kerchove, R.,Daems, D.,De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., Fritz, S., Lesiv, M., Herold, M., Tsendbazar, N.E., Xu, P., Ramoino, F., Arino, O. 76 | - Title: Release of the 10 m WorldCover map 77 | URL: https://blog.vito.be/remotesensing/release-of-the-10-m-worldcover-map 78 | AuthorName: Ruben Van De Kerchove 79 | - Title: ESA WorldCover 10 m 2020 v100 - Product User Manual 80 | URL: https://esa-worldcover.s3.eu-central-1.amazonaws.com/v100/2020/docs/WorldCover_PUM_V1.0.pdf 81 | AuthorName: VITO 82 | - Title: ESA WorldCover 10 m 2021 v200 - Product User Manual 83 | URL: https://esa-worldcover.s3.eu-central-1.amazonaws.com/v200/2021/docs/WorldCover_PUM_V2.0.pdf 84 | AuthorName: VITO 85 | - Title: WorldCover taking it to the next level 86 | URL: https://blog.vito.be/remotesensing/worldcover2021 87 | AuthorName: Ruben Van De Kerchove 88 | - Title: "Fusing GEDI with earth observation data for large area aboveground biomass mapping" 89 | URL: https://doi.org/10.1016/j.jag.2022.103108 90 | AuthorName: Yuri Shendryk 91 | - Title: "The world's most populated and greenest megacities (and how we found out)" 92 | URL: https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/mapping/worlds-greenest-megacities/ 93 | AuthorName: Michael Dangermond, Emily Meriam 94 | 95 | -------------------------------------------------------------------------------- /datasets/glo-30-hand.yaml: -------------------------------------------------------------------------------- 1 | Name: Global 30m Height Above Nearest Drainage (HAND) 2 | Description: > 3 | Height Above Nearest Drainage (HAND) is a terrain model that normalizes topography to the relative heights along the 4 | drainage network and is used to describe the relative soil gravitational potentials or the local drainage potentials. 5 | Each pixel value represents the vertical distance to the nearest drainage. The HAND data provides near-worldwide land 6 | coverage at 30 meters and was produced from the 2021 release of the Copernicus GLO-30 Public DEM as distributed in the 7 | [Registry of Open Data on AWS](https://registry.opendata.aws/copernicus-dem/). 8 | Documentation: https://glo-30-hand.s3.us-west-2.amazonaws.com/readme.html 9 | Contact: https://asf.alaska.edu/asf/contact-us/ 10 | ManagedBy: "[The Alaska Satellite Facility (ASF)](https://asf.alaska.edu/)" 11 | UpdateFrequency: > 12 | None, except HAND may be updated if the[ Copernicus GLO-30 Public](https://registry.opendata.aws/copernicus-dem/) 13 | dataset is updated. 14 | Tags: 15 | - aws-pds 16 | - elevation 17 | - hydrology 18 | - agriculture 19 | - disaster response 20 | - satellite imagery 21 | - geospatial 22 | - cog 23 | - stac 24 | License: > 25 | Copyright 2022 Alaska Satellite Facility (ASF). Produced using the Copernicus WorldDEM™-30 © DLR e.V. 2010-2014 26 | and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights 27 | reserved. The use of the HAND data falls under the terms and conditions of the 28 | [Creative Commons Zero (CC0) 1.0 Universal License](https://creativecommons.org/publicdomain/zero/1.0/). 29 | Citation: 30 | Resources: 31 | - Description: GLO-30 HAND S3 bucket 32 | ARN: arn:aws:s3:::glo-30-hand 33 | Region: us-west-2 34 | Type: S3 Bucket 35 | Explore: 36 | - '[STAC V1.0.0 endpoint](https://stac.asf.alaska.edu/collections/glo-30-hand)' 37 | - '[Via STAC Browser](https://radiantearth.github.io/stac-browser/#/external/stac.asf.alaska.edu/collections/glo-30-hand)' 38 | - Description: Notifications for new data 39 | ARN: arn:aws:sns:us-west-2:879002409890:glo-30-hand-object_created 40 | Region: us-west-2 41 | Type: SNS Topic 42 | DataAtWork: 43 | Tutorials: 44 | - Title: Search the GLO-30 HAND Catalog in Python 45 | URL: https://github.com/ASFHyP3/OpenData/blob/main/glo-30-hand/search-hand-notebook.ipynb 46 | NotebookURL: https://github.com/ASFHyP3/OpenData/blob/main/glo-30-hand/search-hand-notebook.ipynb 47 | AuthorName: Alaska Satellite Facility 48 | AuthorURL: https://asf.alaska.edu/ 49 | - Title: Generate your own HAND data in Python 50 | URL: https://github.com/ASFHyP3/OpenData/blob/main/glo-30-hand/generate-hand-notebook.ipynb 51 | NotebookURL: https://github.com/ASFHyP3/OpenData/blob/main/glo-30-hand/generate-hand-notebook.ipynb 52 | AuthorName: Alaska Satellite Facility 53 | AuthorURL: https://asf.alaska.edu/ 54 | Tools & Applications: 55 | - Title: GLO-30 HAND Webmap 56 | URL: https://asf-daac.maps.arcgis.com/apps/mapviewer/index.html?webmap=6b82a2e4ccd343d5ba73dc04d386e4ee 57 | AuthorName: Alaska Satellite Facility 58 | AuthorURL: https://asf.alaska.edu/ 59 | - Title: GLO-30 HAND ImageServer 60 | URL: https://gis.asf.alaska.edu/arcgis/rest/services/GlobalHAND/GLO30_HAND/ImageServer 61 | AuthorName: Alaska Satellite Facility 62 | AuthorURL: https://asf.alaska.edu/ 63 | Publications: 64 | - Title: 'AGU 2022: A New Global 30m HAND Dataset to Support Hydrological Services and Applications' 65 | URL: https://docs.google.com/presentation/d/1GzHn4bQrqqKj6DFFXwQ9zIvN7-etSjyIpHz3fQmxO0s/edit#slide=id.p 66 | AuthorName: Joseph H. Kennedy, et al. 67 | AuthorURL: https://jhkennedy.org/ 68 | ADXCategories: 69 | - Environmental Data 70 | -------------------------------------------------------------------------------- /datasets/io-lulc.yaml: -------------------------------------------------------------------------------- 1 | Name: 10m Annual Land Use Land Cover (9-class) 2 | Description: | 3 | This dataset, produced by Impact Observatory, Microsoft, and Esri, displays a global map of land use and land cover (LULC) 4 | derived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017 - 2023. 5 | 6 | Each map is a composite of LULC predictions for 9 classes throughout the year 7 | in order to generate a representative snapshot of each year. 8 | 9 | This dataset was generated by Impact Observatory, which used billions of human-labeled pixels 10 | (curated by the National Geographic Society) to train a deep learning model for land classification. 11 | Each global map was produced by applying this model to the Sentinel-2 annual scene collections 12 | from the Mircosoft Planetary Computer. Each of the maps has an assessed average accuracy of over 75%. 13 | 14 | These maps have been improved from Impact Observatory’s previous release and provide 15 | a relative reduction in the amount of anomalous change between classes, 16 | particularly between “Bare” and any of the vegetative classes 17 | “Trees,” “Crops,” “Flooded Vegetation,” and “Rangeland”. 18 | This updated time series of annual global maps is also re-aligned to match the ESA UTM tiling grid for Sentinel-2 imagery. 19 | 20 | Data can be accessed directly from the Registry of Open Data on AWS, from the [STAC 1.0.0 endpoint](https://api.impactobservatory.com/stac-aws/collections/io-10m-annual-lulc/items), or from the [IO Store](https://www.impactobservatory.com/maps-for-good/) for a specific Area of Interest (AOI). 21 | 22 | Documentation: https://www.impactobservatory.com/global_maps 23 | Contact: hello@impactobservatory.com 24 | ManagedBy: "[Impact Observatory](https://www.impactobservatory.com/)" 25 | UpdateFrequency: A new year is made available annually, each January. A new time series was provided July 2023 to reduce anomalous change. 26 | Collabs: 27 | ASDI: 28 | Tags: 29 | - ecosystems 30 | Tags: 31 | - aws-pds 32 | - earth observation 33 | - environmental 34 | - geospatial 35 | - satellite imagery 36 | - sustainability 37 | - stac 38 | - cog 39 | - land cover 40 | - land use 41 | - machine learning 42 | - mapping 43 | - planetary 44 | License: "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)" 45 | Resources: 46 | - Description: 10m Annual Land Use Land Cover (9-class) 47 | ARN: arn:aws:s3:::io-10m-annual-lulc 48 | Region: us-west-2 49 | Type: S3 Bucket 50 | Explore: 51 | - '[STAC 1.0.0 endpoint](https://api.impactobservatory.com/stac-aws/collections/io-10m-annual-lulc/items)' 52 | DataAtWork: 53 | Tools & Applications: 54 | - Title: View the dataset on UN Biodiversity Lab Map Viewer 55 | URL: https://map.unbiodiversitylab.org/earth?basemap=grayscale&coordinates=20,0,2&layers=10m-annual-land-use-land-cover-9-class-01_100 56 | AuthorName: United Nations Development Programme 57 | Publications: 58 | - Title: Global land use / land cover with Sentinel 2 and deep learning 59 | URL: https://ieeexplore.ieee.org/document/9553499 60 | AuthorName: K. Karra, C. Kontgis, Z. Statman-Weil, J. C. Mazzariello, M. Mathis and S. P. Brumby 61 | - Title: Mapping the world with unmatched frequency 62 | URL: https://medium.com/impactobservatoryinc/mapping-the-world-with-unmatched-frequency-54a83527f138 63 | AuthorName: Mark Hannel 64 | - Title: '‘Very Dire’: Devastated by Floods, Pakistan Faces Looming Food Crisis' 65 | URL: https://www.nytimes.com/2022/09/11/world/asia/pakistan-floods-food-crisis.html 66 | AuthorName: New York Times 67 | - Title: These maps from satellite data show how much Earth has changed in only five years 68 | URL: https://www.fastcompany.com/90729824/these-maps-from-satellite-data-show-how-much-earth-has-changed-in-only-five-years 69 | AuthorName: Fast Company 70 | - Title: The world's most populated and greenest megacities (and how we found out) 71 | URL: https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/mapping/worlds-greenest-megacities/ 72 | AuthorName: Esri 73 | -------------------------------------------------------------------------------- /datasets/jaxa-alos-palsar2-scansar.yaml: -------------------------------------------------------------------------------- 1 | Name: PALSAR-2 ScanSAR CARD4L (L2.2) 2 | Description: | 3 | The 25 m PALSAR-2 ScanSAR is normalized backscatter data of PALSAR-2 broad area observation mode with observation width of 350 km. 4 | The SAR imagery was ortho-rectificatied and slope corrected using the ALOS World 3D - 30 m (AW3D30) Digital Surface Model. 5 | Polarization data are stored as 16-bit digital numbers (DN). 6 | The DN values can be converted to gamma naught values in decibel unit (dB) using the following equation: 7 | γ0 = 10*log10(DN2) - 83.0 dB 8 | CARD4L stands for CEOS Analysis Ready Data for Land (Level 2.2) data are ortho-rectified and radiometrically terrain-corrected. 9 | This dataset is compatible with the [Committee on Earth Observation (CEOS)](https://ceos.org/) [Analysis Ready Data for LAND (CARD4L)](https://ceos.org/ard/files/PFS/NRB/v5.5/CARD4L-PFS_NRB_v5.5.pdf) standard.

10 | 11 | Documentation: https://www.eorc.jaxa.jp/ALOS/en/dataset/palsar2_l22_e.htm 12 | Contact: aproject@jaxa.jp 13 | ManagedBy: "[JAXA](https://www.jaxa.jp/)" 14 | UpdateFrequency: Every month after 42 days observed 15 | Collabs: 16 | ASDI: 17 | Tags: 18 | - satellite imagery 19 | Tags: 20 | - aws-pds 21 | - agriculture 22 | - earth observation 23 | - satellite imagery 24 | - geospatial 25 | - natural resource 26 | - sustainability 27 | - disaster response 28 | - synthetic aperture radar 29 | - deafrica 30 | - stac 31 | - cog 32 | License: | 33 | Data is available for free under the [terms of use](https://earth.jaxa.jp/policy/en.html). 34 | Resources: 35 | - Description: PALSAR-2 ScanSAR CARD4L 36 | ARN: arn:aws:s3:::jaxaalos2/palsar2/L2.2/Africa/ 37 | Region: us-west-2 38 | Type: S3 Bucket 39 | RequesterPays: False 40 | DataAtWork: 41 | Tutorials: 42 | Tools & Applications: 43 | Publications: 44 | - Title: "ALOS series Open and Free Data" 45 | URL: https://www.eorc.jaxa.jp/ALOS/en/dataset/alos_open_and_free_e.htm 46 | AuthorName: JAXA EORC 47 | 48 | -------------------------------------------------------------------------------- /datasets/jaxa-usgs-nasa-kaguya-tc-dtms.yaml: -------------------------------------------------------------------------------- 1 | Name: JAXA / USGS / NASA Kaguya/SELENE Terrain Camera Digital Terrain Models 2 | Description: | 3 | The Japan Aerospace EXploration Agency (JAXA) SELenological and ENgineering Explorer (SELENE) mission’s Kaguya spacecraft was launched on September 14, 2007 and science operations around the Moon started October 20, 2007. The primary mission in a circular polar orbit 100-km above the surface lasted from October 20, 2007 until October 31, 2008. An extended mission was then conducted in lower orbits (averaging 50km above the surface) from November 1, 2008 until the SELENE mission ended with Kaguya impacting the Moon on June 10, 2009. These data are digital terrain models derived using the NASA Ames Stereo Pipeline (ASP) and the Kaguya stereoscopic data. Digital terrain models (DTMs) in this data set were bundle adjusted and aligned to Lunar Orbiter Laser Altimeter (LOLA) shot data. The sensor model intrinsics used for these data have been re-estimated to reduce inter-DTM horizontal and vertical errors. Data are controlled to LOLA using the ASP pc_align program. Data co-register at orthoimage resolution (11-37 meters per pixel). At image resolution horizontal offsets are measurable. Horizontal precision is measures to be better than 30 meters per pixel on average. Vertical errors are mean centered to zero. An assessment of overlapping DTMs showed vertical precision on the order of 4 meters. Spacecraft jitter and unmodelled lense distortion at the observation edges is believed to be the major contributor to the vertical errors. To create these analysis ready data, we have taken the ASP genreated data, map projected the data to a stereopair centered orthographic projection and converted to a Cloud Optimized GeoTiff (COG) for online streaming. These data use a priori spacecraft ephemerides (for the nominal mission) and improved, but not controlled spacecraft ephemerides for the extended mission. These data co-register with other LOLA controlled data to the aforementioned horizontal and vertical accuracies. 4 | Documentation: https://stac.astrogeology.usgs.gov/docs/data/moon/kaguyatc/ 5 | Contact: https://answers.usgs.gov/ 6 | ManagedBy: "[NASA](https://www.nasa.gov)" 7 | UpdateFrequency: The Kaguya/SELENE mission has completed. At least one update to this dataset is planned to address identified issues with the nominal swath width evening observations. 8 | Tags: 9 | - aws-pds 10 | - planetary 11 | - elevation 12 | - stac 13 | - cog 14 | License: "[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)" 15 | Citation: https://doi.org/10.5066/P13D7VMG 16 | Resources: 17 | - Description: Digital terrain models, orthoimages, shaded reliefs, and quality assurance documents 18 | ARN: arn:aws:s3:::astrogeo-ard/moon/kaguya/terrain_camera/usgs_dtms/ 19 | Region: us-west-2 20 | Type: S3 Bucket 21 | Explore: 22 | - '[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/kaguya_terrain_camera_usgs_dtms)' 23 | DataAtWork: 24 | Tutorials: 25 | - Title: "Discovering and Downloading Data via the Command Line" 26 | URL: https://stac.astrogeology.usgs.gov/docs/tutorials/cli/ 27 | AuthorName: J. Laura 28 | AuthorURL: https://astrogeology.usgs.gov 29 | - Title: "Discovering and Downloading Data with Python" 30 | URL: https://stac.astrogeology.usgs.gov/docs/tutorials/basicpython/ 31 | AuthorName: J. Laura 32 | AuthorURL: https://astrogeology.usgs.gov 33 | - Title: "Querying for Data in an ROI and Loading it into QGIS" 34 | URL: https://stac.astrogeology.usgs.gov/docs/examples/to_qgis/ 35 | AuthorName: J. Laura 36 | AuthorURL: https://astrogeology.usgs.gov 37 | Tools & Applications: 38 | - Title: PySTAC Client 39 | URL: https://github.com/stac-utils/pystac-client 40 | AuthorName: PySTAC-Client Contributors 41 | AuthorURL: https://github.com/stac-utils/pystac-client/graphs/contributors 42 | -------------------------------------------------------------------------------- /datasets/jaxa-usgs-nasa-kaguya-tc.yaml: -------------------------------------------------------------------------------- 1 | Name: JAXA / USGS / NASA Kaguya/SELENE Terrain Camera Observations 2 | Description: | 3 | The Japan Aerospace EXploration Agency (JAXA) SELenological and ENgineering Explorer (SELENE) mission’s Kaguya spacecraft was launched on September 14, 2007 and science operations around the Moon started October 20, 2007. The primary mission in a circular polar orbit 100-km above the surface lasted from October 20, 2007 until October 31, 2008. An extended mission was then conducted in lower orbits (averaging 50km above the surface) from November 1, 2008 until the SELENE mission ended with Kaguya impacting the Moon on June 10, 2009. These data were collected in monoscopic observing mode. To create these analysis ready data, we have taken the JAXA Data ARchives and Transmission System (DARTS) archived data, map projected the data to equirectangular or polar stereographic (pole centered) based on the center latitude of the observation, and converted to a Cloud Optimized GeoTiff (COG) for online streaming. These data use a priori spacecraft ephemerides (for the nominal mission) and improved, but not controlled spacecraft ephemerides for the extended mission. Therefore, these uncontrolled data are not guaranteed to co-register with other data sets. 4 | Documentation: https://stac.astrogeology.usgs.gov/docs/data/moon/kaguyatc/ 5 | Contact: https://answers.usgs.gov/ 6 | ManagedBy: "[NASA](https://www.nasa.gov)" 7 | UpdateFrequency: The Kaguya/SELENE mission has completed. No updates to this dataset are planned. 8 | Tags: 9 | - aws-pds 10 | - planetary 11 | - satellite imagery 12 | - stac 13 | - cog 14 | License: "[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)" 15 | Citation: https://doi.org/10.5066/P9SH5YNV 16 | Resources: 17 | - Description: Scenes and metadata for monoscopic observing mode 18 | ARN: arn:aws:s3:::astrogeo-ard/moon/kaguya/terrain_camera/monoscopic/uncontrolled/ 19 | Region: us-west-2 20 | Type: S3 Bucket 21 | Explore: 22 | - '[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/kaguya_terrain_camera_monoscopic_uncontrolled_observations)' 23 | - Description: Scenes and metadata for stereoscopic observing mode 24 | ARN: arn:aws:s3:::astrogeo-ard/moon/kaguya/terrain_camera/stereoscopic/uncontrolled/ 25 | Region: us-west-2 26 | Type: S3 Bucket 27 | Explore: 28 | - '[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/kaguya_terrain_camera_stereoscopic_uncontrolled_observations)' 29 | - Description: Scenes and metadata for spectral profiler (spsupport) observing mode 30 | ARN: arn:aws:s3:::astrogeo-ard/moon/kaguya/terrain_camera/spsupport/uncontrolled/ 31 | Region: us-west-2 32 | Type: S3 Bucket 33 | Explore: 34 | - '[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/kaguya_terrain_camera_spsupport_uncontrolled_observations)' 35 | DataAtWork: 36 | Tutorials: 37 | - Title: "Discovering and Downloading Data via the Command Line" 38 | URL: https://stac.astrogeology.usgs.gov/docs/tutorials/cli/ 39 | AuthorName: J. Laura 40 | AuthorURL: https://astrogeology.usgs.gov 41 | - Title: "Discovering and Downloading Data with Python" 42 | URL: https://stac.astrogeology.usgs.gov/docs/tutorials/basicpython/ 43 | AuthorName: J. Laura 44 | AuthorURL: https://astrogeology.usgs.gov 45 | - Title: "Querying for Data in an ROI and Loading it into QGIS" 46 | URL: https://stac.astrogeology.usgs.gov/docs/examples/to_qgis/ 47 | AuthorName: J. Laura 48 | AuthorURL: https://astrogeology.usgs.gov 49 | Tools & Applications: 50 | - Title: PySTAC Client 51 | URL: https://github.com/stac-utils/pystac-client 52 | AuthorName: PySTAC-Client Contributors 53 | AuthorURL: https://github.com/stac-utils/pystac-client/graphs/contributors 54 | -------------------------------------------------------------------------------- /datasets/jaxa-usgs-nasa-kaguya-tc_monoscopic.yaml: -------------------------------------------------------------------------------- 1 | Name: JAXA / USGS / NASA Kaguya/SELENE Terrain Camera Monoscopic Observations 2 | Description: | 3 | The Japan Aerospace EXploration Agency (JAXA) SELenological and ENgineering Explorer (SELENE) mission’s Kaguya spacecraft was launched on September 14, 2007 and science operations around the Moon started October 20, 2007. The primary mission in a circular polar orbit 100-km above the surface lasted from October 20, 2007 until October 31, 2008. An extended mission was then conducted in lower orbits (averaging 50km above the surface) from November 1, 2008 until the SELENE mission ended with Kaguya impacting the Moon on June 10, 2009. These data were collected in monoscopic observing mode. To create these analysis ready data, we have taken the JAXA Data ARchives and Transmission System (DARTS) archived data, map projected the data to equirectangular or polar stereographic (pole centered) based on the center latitude of the observation, and converted to a Cloud Optimized GeoTiff (COG) for online streaming. These data use a priori spacecraft ephemerides (for the nominal mission) and improved, but not controlled spacecraft ephemerides for the extended mission. Therefore, these uncontrolled data are not guaranteed to co-register with other data sets. 4 | Documentation: https://stac.astrogeology.usgs.gov/docs/data/moon/kaguyatc/ 5 | Contact: https://answers.usgs.gov/ 6 | ManagedBy: "[NASA](https://www.nasa.gov)" 7 | UpdateFrequency: The Kaguya/SELENE has completed. No updates to this dataset are planned. 8 | Tags: 9 | - aws-pds 10 | - planetary 11 | - satellite imagery 12 | - stac 13 | - cog 14 | License: "[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)" 15 | Citation: https://doi.org/10.5066/P9SH5YNV 16 | Resources: 17 | - Description: Scenes and metadata 18 | ARN: arn:aws:s3:::astrogeo-ard/moon/kaguya/terrain_camera/monoscopic/uncontrolled/ 19 | Region: us-west-2 20 | Type: S3 Bucket 21 | Explore: 22 | - '[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/kaguya_terrain_camera_monoscopic_uncontrolled_observations)' 23 | DataAtWork: 24 | Tutorials: 25 | - Title: "Discovering and Downloading Data via the Command Line" 26 | URL: https://stac.astrogeology.usgs.gov/docs/tutorials/cli/ 27 | AuthorName: J. Laura 28 | AuthorURL: https://astrogeology.usgs.gov 29 | - Title: "Discovering and Downloading Data with Python" 30 | URL: https://stac.astrogeology.usgs.gov/docs/tutorials/basicpython/ 31 | AuthorName: J. Laura 32 | AuthorURL: https://astrogeology.usgs.gov 33 | - Title: "Querying for Data in an ROI and Loading it into QGIS" 34 | URL: https://stac.astrogeology.usgs.gov/docs/examples/to_qgis/ 35 | AuthorName: J. Laura 36 | AuthorURL: https://astrogeology.usgs.gov 37 | Tools & Applications: 38 | - Title: PySTAC Client 39 | URL: https://github.com/stac-utils/pystac-client 40 | AuthorName: PySTAC-Client Contributors 41 | AuthorURL: https://github.com/stac-utils/pystac-client/graphs/contributors 42 | -------------------------------------------------------------------------------- /datasets/maxar-open-data.yaml: -------------------------------------------------------------------------------- 1 | Name: Maxar Open Data Program 2 | Description: | 3 | Pre and post event high-resolution satellite imagery in support of emergency planning, risk assessment, 4 | monitoring of staging areas and emergency response, damage assessment, and recovery. These images are generated 5 | using the [Maxar ARD](https://ard.maxar.com/docs) pipeline, tiled on an organized grid in analysis-ready 6 | cloud-optimized formats. 7 | Documentation: https://www.maxar.com/open-data 8 | Contact: https://www.maxar.com/open-data 9 | UpdateFrequency: New data is released in response to activations. Older data may be migrated to the ARD format as needed. 10 | Collabs: 11 | ASDI: 12 | Tags: 13 | - disaster response 14 | Tags: 15 | - aws-pds 16 | - earth observation 17 | - disaster response 18 | - geospatial 19 | - satellite imagery 20 | - cog 21 | - stac 22 | License: Creative Commons Attribution Non Commercial 4.0 23 | ManagedBy: "[Maxar](https://www.maxar.com/)" 24 | Resources: 25 | - Description: Imagery and metadata 26 | ARN: arn:aws:s3:::maxar-opendata 27 | Region: us-west-2 28 | Type: S3 Bucket 29 | Explore: 30 | - '[STAC Browser](https://radiantearth.github.io/stac-browser/#/external/maxar-opendata.s3.dualstack.us-west-2.amazonaws.com/events/catalog.json)' 31 | - '[STAC Catalog](https://stacindex.org/catalogs/maxar-open-data-catalog-ard-format#/)' 32 | DataAtWork: 33 | Tutorials: 34 | - Title: ARD Deliverables and File Structure 35 | URL: https://ard.maxar.com/docs/ard-order-delivery/about-ard-order-delivery/ 36 | AuthorName: Maxar Open Data 37 | AuthorURL: https://www.maxar.com/open-data 38 | - Title: ARD and Command Line Tools 39 | URL: https://ard.maxar.com/docs/working-with-ard/command-line-tools/ 40 | AuthorName: Maxar Open Data 41 | AuthorURL: https://www.maxar.com/open-data 42 | - Title: "Data Access (SDK tutorial)" 43 | URL: https://ard.maxar.com/docs/sdk/sdk/data-access/ 44 | AuthorName: Maxar Open Data 45 | AuthorURL: https://www.maxar.com/open-data 46 | - Title: "Visualizing Maxar Open Data with SageMaker Studio Lab" 47 | URL: https://github.com/giswqs/maxar-open-data/ 48 | NotebookURL: https://github.com/giswqs/maxar-open-data/blob/master/examples/maxar_open_data.ipynb 49 | AuthorName: Qiusheng Wu 50 | AuthorURL: https://geography.utk.edu/about-us/faculty/dr-qiusheng-wu/ 51 | Services: 52 | - Amazon SageMaker Studio Lab 53 | - Title: "Visualizing Turkey & Syria Earthquake Maxar Open Data with SageMaker Studio Lab" 54 | URL: https://github.com/giswqs/maxar-open-data/ 55 | NotebookURL: https://github.com/giswqs/maxar-open-data/blob/master/examples/turkey_earthquake.ipynb 56 | AuthorName: Qiusheng Wu 57 | AuthorURL: https://geography.utk.edu/about-us/faculty/dr-qiusheng-wu/ 58 | Services: 59 | - Amazon SageMaker Studio Lab 60 | - Title: "Maxar Open Data - Leafmap" 61 | URL: https://leafmap.org/notebooks/67_maxar_open_data/ 62 | AuthorName: Qiusheng Wu 63 | AuthorURL: https://geography.utk.edu/about-us/faculty/dr-qiusheng-wu/ 64 | Tools & Applications: 65 | - Title: Maxar ARD SDK (max-ard) 66 | URL: https://ard.maxar.com/docs/sdk/ 67 | AuthorName: Maxar Open Data 68 | AuthorURL: https://www.maxar.com/open-data 69 | - Title: MGP Xpress 70 | URL: https://xpress.maxar.com/ 71 | AuthorName: MGP Xpress 72 | AuthorURL: https://xpress-docs.maxar.com/Home.htm# 73 | Publications: 74 | - Title: "Using Data from Earth Observation to Support Sustainable Development Indicators: An Analysis of the Literature and Challenges for the Future" 75 | URL: https://doi.org/10.3390/su14031191 76 | AuthorName: Ana Andries, Stephen Morse, Richard J. Murphy, Jim Lynch, and Emma R. Woolliams 77 | - Title: "Disaster, Infrastructure and Participatory Knowledge The Planetary Response Network" 78 | URL: https://eprints.lancs.ac.uk/id/eprint/167032/1/Simmons_et_al_2022_CSTP_PRN_Paper_Special_Issue_Accepted_Version.pdf 79 | AuthorName: Brooke Simmons, Chris Lintott, Steven Reece, et al. 80 | - Title: " Machine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami" 81 | URL: https://doi.org/10.1080/19475705.2022.2147455 82 | AuthorName: Riantini Virtriana et al. 83 | -------------------------------------------------------------------------------- /datasets/nasa-usgs-controlled-mro-ctx-dtms.yaml: -------------------------------------------------------------------------------- 1 | Name: NASA / USGS Mars Reconnaissance Orbiter (MRO) Context Camera (CTX) Targeted DTMs 2 | Description: | 3 | As of March, 2023 the Mars Reconnaissance Orbiter (MRO) High Resolution Science Experiment (HiRISE) sensor has collected more than 5000 targeted stereopairs. During HiRISE acquisition, the Context Camera (CTX) also collects lower resolution, higher spatial extent context images. These CTX acquisitions are also targeted stereopairs. This data set contains targeted CTX DTMs and orthoimages, created using the NASA Ames Stereopipeline. These data have been created using relatively controlled CTX images that have been globally bundle adjusted using the USGS Integrated System for Imagers and Spectrometers (ISIS) jigsaw application. Relative control at global scale reduces common issues such as spacecraft jitter in the resulting DTMs. DTMs were aligned as part of 26 different groupings to the ultimate MOLA product using an iterative pc_align approach. Therefore, all DTMs and orthoimages are absolutely controlled to MOLA, a proxy product for the Mars geodetic coordinate reference frame. 4 | Documentation: https://stac.astrogeology.usgs.gov/docs/data/mars/ctxdtms/ 5 | Contact: https://answers.usgs.gov/ 6 | ManagedBy: "[NASA](https://www.nasa.gov)" 7 | UpdateFrequency: Updated as new stereoapirs are processed 8 | Tags: 9 | - aws-pds 10 | - planetary 11 | - satellite imagery 12 | - elevation 13 | - stac 14 | - cog 15 | License: "[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)" 16 | Citation: https://doi.org/10.5066/P9JKVWR3 17 | Resources: 18 | - Description: DTMs, orthoimages, error images, and quality assurance metrics 19 | ARN: arn:aws:s3:::astrogeo-ard/mars/mro/ctx/controlled/usgs/ 20 | Region: us-west-2 21 | Type: S3 Bucket 22 | Explore: 23 | - '[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/collections/mro_ctx_controlled_usgs_dtms)' 24 | DataAtWork: 25 | Tutorials: 26 | - Title: "Discovering and Downloading Data via the Command Line" 27 | URL: https://stac.astrogeology.usgs.gov/docs/tutorials/cli/ 28 | AuthorName: J. Laura 29 | AuthorURL: https://astrogeology.usgs.gov 30 | - Title: "Discovering and Downloading Data with Python" 31 | URL: https://stac.astrogeology.usgs.gov/docs/tutorials/basicpython/ 32 | AuthorName: J. Laura 33 | AuthorURL: https://astrogeology.usgs.gov 34 | - Title: "Querying for Data in an ROI and Loading it into QGIS" 35 | URL: https://stac.astrogeology.usgs.gov/docs/examples/to_qgis/ 36 | AuthorName: J. Laura 37 | AuthorURL: https://astrogeology.usgs.gov 38 | Tools & Applications: 39 | - Title: PySTAC Client 40 | URL: https://github.com/stac-utils/pystac-client 41 | AuthorName: PySTAC-Client Contributors 42 | AuthorURL: https://github.com/stac-utils/pystac-client/graphs/contributors 43 | -------------------------------------------------------------------------------- /datasets/nasa-usgs-europa-dtms.yaml: -------------------------------------------------------------------------------- 1 | Name: NASA / USGS Controlled Europa DTMs 2 | Description: | 3 | Knowledge of a planetary surface’s topography is necessary to understand its geology and enable landed mission operations. The Solid State Imager (SSI) on board NASA’s Galileo spacecraft acquired more than 700 images of Jupiter’s moon Europa. Although moderate- and high-resolution coverage is extremely limited, repeat coverage of a small number of sites enables the creation of digital terrain models (DTMs) via stereophotogrammetry. Here we provide stereo-derived DTMs of five sites on Europa. The sites are the bright band Agenor Linea, the crater Cilix, the crater Pwyll, pits and chaos adjacent to Rhadamanthys Linea, and ridged plains near Yelland Linea. We generated the DTMs using BAE’s SOCET SET® software and each was manually edited to correct identifiable errors from the automated stereo matching process. Additionally, we used the recently updated image pointing information provided by the U.S. Geological Survey (Bland, et al., 2021), which ties the DTMs to an existing horizontal datum and enables the DTMs to easily be used in coordination with that globally controlled image set. The DTMs are of the highest quality achievable with Galileo data and are therefore suitable for most scientific analysis. However, there are inherent uncertainties in the DTMs including horizontal resolutions that are typically 1–2 km (~10x the image pixel scale) and expected vertical precision (the root mean square (RMS) uncertainty in a point elevation) of 10s–100s of m. The DTMs and their uncertainties are discussed in detail in the documentation. 4 | Documentation: https://stac.astrogeology.usgs.gov/docs/data/jupiter/europa/europa_controlled_usgs_dtms/ 5 | Contact: https://answers.usgs.gov/ 6 | ManagedBy: "[NASA](https://www.nasa.gov)" 7 | UpdateFrequency: HiRISE data will be updated as new releases are made to the Planetary Data System. 8 | Tags: 9 | - aws-pds 10 | - planetary 11 | - satellite imagery 12 | - stac 13 | - cog 14 | License: "[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)" 15 | Citation: https://doi.org/10.5066/P99HX1H3 16 | Resources: 17 | - Description: Scenes and metadata 18 | ARN: arn:aws:s3:::astrogeo-ard/jupiter/europa/galileo_voyager/usgs_controlled_dtms/ 19 | Region: us-west-2 20 | Type: S3 Bucket 21 | Explore: 22 | - '[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/galileo_usgs_photogrammetrically_controlled_dtms?.language=en)' 23 | DataAtWork: 24 | Tutorials: 25 | - Title: "Discovering and Downloading Data via the Command Line" 26 | URL: https://stac.astrogeology.usgs.gov/docs/tutorials/cli/ 27 | AuthorName: J. Laura 28 | AuthorURL: https://astrogeology.usgs.gov 29 | - Title: "Discovering and Downloading Data with Python" 30 | URL: https://stac.astrogeology.usgs.gov/docs/tutorials/basicpython/ 31 | AuthorName: J. Laura 32 | AuthorURL: https://astrogeology.usgs.gov 33 | - Title: "Querying for Data in an ROI and Loading it into QGIS" 34 | URL: https://stac.astrogeology.usgs.gov/docs/examples/to_qgis/ 35 | AuthorName: J. Laura 36 | AuthorURL: https://astrogeology.usgs.gov 37 | Tools & Applications: 38 | - Title: PySTAC Client 39 | URL: https://github.com/stac-utils/pystac-client 40 | AuthorName: PySTAC-Client Contributors 41 | AuthorURL: https://github.com/stac-utils/pystac-client/graphs/contributors 42 | -------------------------------------------------------------------------------- /datasets/nasa-usgs-europa-mosaics.yaml: -------------------------------------------------------------------------------- 1 | Name: NASA / USGS Europa Controlled Observation Mosaics 2 | Description: | 3 | The Solid State Imager (SSI) on NASA's Galileo spacecraft acquired more than 500 images of Jupiter's moon, Europa. These images vary from relatively low-resolution hemispherical imaging, to high-resolution targeted images that cover a small portion of the surface. Here we provide a set of 92 image mosaics generated from minimally processed, projected Galileo images with photogrammetrically improved locations on Europa's surface.

4 | These images provide users with nearly the entire Galileo Europa imaging dataset at its native resolution and with improved relative image locations. The Solid State Imager on NASA's Galileo spacecraft provided the only moderate- to high-resolution images of Jupiter's moon, Europa. Unfortunately, uncertainty in the position and pointing of the spacecraft, as well as the position and orientation of Europa, when the images were acquired resulted in significant errors in image locations on the surface. The result of these errors is that images acquired during different Galileo orbits, or even at different times during the same orbit, are significantly misaligned (errors of up to 100 km on the surface).

5 | The dataset provides a set of individual images that can be used for scientific analysis and mission planning activities. 6 | Documentation: https://stac.astrogeology.usgs.gov/docs/data/jupiter/europa/galileo_sequence_mosaics/ 7 | Contact: https://answers.usgs.gov/ 8 | ManagedBy: "[NASA](https://www.nasa.gov)" 9 | UpdateFrequency: No future updates planned. 10 | Tags: 11 | - aws-pds 12 | - planetary 13 | - satellite imagery 14 | - stac 15 | - cog 16 | License: "[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)" 17 | Citation: https://doi.org/10.5066/P9VKKK7C 18 | Resources: 19 | - Description: Scenes and metadata 20 | ARN: arn:aws:s3:::astrogeo-ard/jupiter/europa/galileo_voyager/usgs_controlled_mosaics/ 21 | Region: us-west-2 22 | Type: S3 Bucket 23 | Explore: 24 | - '[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/galileo_usgs_photogrammetrically_controlled_mosaics)' 25 | DataAtWork: 26 | Tutorials: 27 | - Title: "Discovering and Downloading Data via the Command Line" 28 | URL: https://stac.astrogeology.usgs.gov/docs/tutorials/cli/ 29 | AuthorName: J. Laura 30 | AuthorURL: https://astrogeology.usgs.gov 31 | - Title: "Discovering and Downloading Data with Python" 32 | URL: https://stac.astrogeology.usgs.gov/docs/tutorials/basicpython/ 33 | AuthorName: J. Laura 34 | AuthorURL: https://astrogeology.usgs.gov 35 | - Title: "Querying for Data in an ROI and Loading it into QGIS" 36 | URL: https://stac.astrogeology.usgs.gov/docs/examples/to_qgis/ 37 | AuthorName: J. Laura 38 | AuthorURL: https://astrogeology.usgs.gov 39 | Tools & Applications: 40 | - Title: PySTAC Client 41 | URL: https://github.com/stac-utils/pystac-client 42 | AuthorName: PySTAC-Client Contributors 43 | AuthorURL: https://github.com/stac-utils/pystac-client/graphs/contributors 44 | -------------------------------------------------------------------------------- /datasets/nasa-usgs-europa-observations.yaml: -------------------------------------------------------------------------------- 1 | Name: NASA / USGS Europa Controlled Observations 2 | Description: | 3 | The Solid State Imager (SSI) on NASA's Galileo spacecraft acquired more than 500 images of Jupiter's moon, Europa. These images vary from relatively low-resolution hemispherical imaging, to high-resolution targeted images that cover a small portion of the surface. Here we provide a set of 481 minimally processed, projected Galileo images with photogrammetrically improved locations on Europa's surface. These individual images were subsequently used as input into a set of 92 observation mosaics.

4 | These images provide users with nearly the entire Galileo Europa imaging dataset at its native resolution and with improved relative image locations. The Solid State Imager on NASA's Galileo spacecraft provided the only moderate- to high-resolution images of Jupiter's moon, Europa. Unfortunately, uncertainty in the position and pointing of the spacecraft, as well as the position and orientation of Europa, when the images were acquired resulted in significant errors in image locations on the surface. The result of these errors is that images acquired during different Galileo orbits, or even at different times during the same orbit, are significantly misaligned (errors of up to 100 km on the surface).

5 | The dataset provides a set of individual images that can be used for scientific analysis and mission planning activities. 6 | Documentation: https://stac.astrogeology.usgs.gov/docs/data/jupiter/europa/galileo_individual_images/ 7 | Contact: https://answers.usgs.gov/ 8 | ManagedBy: "[NASA](https://www.nasa.gov)" 9 | UpdateFrequency: No future updates planned. 10 | Tags: 11 | - aws-pds 12 | - planetary 13 | - satellite imagery 14 | - stac 15 | - cog 16 | License: "[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)" 17 | Citation: https://doi.org/10.5066/P9VKKK7C 18 | Resources: 19 | - Description: Scenes and metadata 20 | ARN: arn:aws:s3:::astrogeo-ard/jupiter/europa/galileo_voyager/usgs_controlled_observations/ 21 | Region: us-west-2 22 | Type: S3 Bucket 23 | Explore: 24 | - '[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/galileo_usgs_photogrammetrically_controlled_observations)' 25 | DataAtWork: 26 | Tutorials: 27 | - Title: "Discovering and Downloading Data via the Command Line" 28 | URL: https://stac.astrogeology.usgs.gov/docs/tutorials/cli/ 29 | AuthorName: J. Laura 30 | AuthorURL: https://astrogeology.usgs.gov 31 | - Title: "Discovering and Downloading Data with Python" 32 | URL: https://stac.astrogeology.usgs.gov/docs/tutorials/basicpython/ 33 | AuthorName: J. Laura 34 | AuthorURL: https://astrogeology.usgs.gov 35 | - Title: "Querying for Data in an ROI and Loading it into QGIS" 36 | URL: https://stac.astrogeology.usgs.gov/docs/examples/to_qgis/ 37 | AuthorName: J. Laura 38 | AuthorURL: https://astrogeology.usgs.gov 39 | Tools & Applications: 40 | - Title: PySTAC Client 41 | URL: https://github.com/stac-utils/pystac-client 42 | AuthorName: PySTAC-Client Contributors 43 | AuthorURL: https://github.com/stac-utils/pystac-client/graphs/contributors 44 | -------------------------------------------------------------------------------- /datasets/nasa-usgs-lunar-orbiter-laser-altimeter.yaml: -------------------------------------------------------------------------------- 1 | Name: NASA / USGS Lunar Orbiter Laser Altimeter Cloud Optimized Point Cloud 2 | Description: | 3 | The lunar orbiter laser altimeter (LOLA) has collected and released almost 7 billion individual laser altimeter returns from the lunar surface. This dataset includes individual altimetry returns scraped from the Planetary Data System (PDS) LOLA Reduced Data Record (RDR) Query Tool, V2.0. Data are organized in 15˚ x 15˚ (longitude/latitude) sections, compressed and encoded into the Cloud Optimized Point Cloud (COPC) file format, and collected into a Spatio-Temporal Asset Catalog (STAC) collection for query and analysis. The data are in latitude, longitude, and radius (X, Y, Z) format with the proper IAU 2015 30100 well-known text projection string. These data are in the -180 to 180, center longitude 0 domain. Users of this data set are encouraged to use the Point Data Abstract Library (PDAL) STAC driver to query at the collection level or the COPC driver to query individual COPC tiles within the dataset. Queries of these data using bounding boxes, buffered points, or other geometries should use the -180 to 180 longitude domain (converting from 0-360, clone 180 as needed). 4 | Documentation: https://stac.astrogeology.usgs.gov/docs/data/moon/lola/ 5 | Contact: https://answers.usgs.gov/ 6 | ManagedBy: "[NASA](https://www.nasa.gov)" 7 | UpdateFrequency: Intermittent as new LOLA RDR data are released and processing capabilities become available. 8 | Tags: 9 | - aws-pds 10 | - planetary 11 | - elevation 12 | - lidar 13 | - stac 14 | License: "[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)" 15 | Citation: https://doi.org/10.5066/P9V5JIWH 16 | Resources: 17 | - Description: Lunar Orbiter Laser Altimeter (LOLA) Reduced Data Record (RDR) point cloud in Cloud Optimized Point Cloud (COPC) format. 18 | ARN: arn:aws:s3:::astrogeo-ard/moon/lro/lola/ 19 | Region: us-west-2 20 | Type: S3 Bucket 21 | Explore: 22 | - '[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/lunar_orbiter_laser_altimeter)' 23 | DataAtWork: 24 | Tutorials: 25 | - Title: "Discovering and Downloading Data via the Command Line" 26 | URL: https://stac.astrogeology.usgs.gov/docs/tutorials/cli/ 27 | AuthorName: J. Laura 28 | AuthorURL: https://astrogeology.usgs.gov 29 | - Title: "Discovering and Downloading Data with Python" 30 | URL: https://stac.astrogeology.usgs.gov/docs/tutorials/basicpython/ 31 | AuthorName: J. Laura 32 | AuthorURL: https://astrogeology.usgs.gov 33 | - Title: "Querying for Data in an ROI and Loading it into QGIS" 34 | URL: https://stac.astrogeology.usgs.gov/docs/examples/to_qgis/ 35 | AuthorName: J. Laura 36 | AuthorURL: https://astrogeology.usgs.gov 37 | Tools & Applications: 38 | - Title: PySTAC Client 39 | URL: https://github.com/stac-utils/pystac-client 40 | AuthorName: PySTAC-Client Contributors 41 | AuthorURL: https://github.com/stac-utils/pystac-client/graphs/contributors 42 | - Title: Point Data Abstraction Library (PDAL) 43 | URL: https://pdal.io/ 44 | AuthorName: PDAL Contributors 45 | AuthorURL: https://github.com/PDAL/PDAL 46 | -------------------------------------------------------------------------------- /datasets/nasa-usgs-mars-hirise-dtms.yaml: -------------------------------------------------------------------------------- 1 | Name: NASA / USGS Released HiRISE Digital Terrain Models 2 | Description: | 3 | These data are digital terrain models (DTMs) created by multiple different institutions and released to the Planetary Data System (PDS) by the University of Arizona. The data are processed from the Planetary Data System (PDS) stored JP2 files, map projected, and converted to Cloud Optimized GeoTiffs (COGs) for efficient remote data access. These data are controlled to the Mars Orbiter Laser Altimeter (MOLA). Therefore, they are a proxy for the geodetic coordinate reference frame. These data are not guaranteed to co-register with an uncontrolled products (e.g., the uncontrolled High Resolution Science Imaging Experiment (HiRISE) Reduced Data Record (RDR) data). Data are released using simple cylindrical (planetocentric positive East, center longitude 0, -180 - 180 longitude domain) or a pole centered polar stereographic projection. Data are projected to the appropriate IAU Well-known Text v2 (WKT2) represented projection. 4 | Documentation: https://stac.astrogeology.usgs.gov/docs/data/mars/hirise_dtms/ 5 | Contact: https://answers.usgs.gov/ 6 | ManagedBy: "[NASA](https://www.nasa.gov)" 7 | UpdateFrequency: HiRISE DTMs will be updated as new releases are made by the University of Arizona to the Planetary Data System. 8 | Tags: 9 | - aws-pds 10 | - planetary 11 | - satellite imagery 12 | - stac 13 | - cog 14 | License: "[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)" 15 | Citation: https://doi.org/10.5066/P9VQTS7V 16 | Resources: 17 | - Description: Scenes and metadata 18 | ARN: arn:aws:s3:::astrogeo-ard/mars/mro/hirise/controlled/dtm 19 | Region: us-west-2 20 | Type: S3 Bucket 21 | Explore: 22 | - '[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/mro_hirise_socet_dtms)' 23 | DataAtWork: 24 | Tutorials: 25 | - Title: "Discovering and Downloading Data via the Command Line" 26 | URL: https://stac.astrogeology.usgs.gov/docs/tutorials/cli/ 27 | AuthorName: J. Laura 28 | AuthorURL: https://astrogeology.usgs.gov 29 | - Title: "Discovering and Downloading Data with Python" 30 | URL: https://stac.astrogeology.usgs.gov/docs/tutorials/basicpython/ 31 | AuthorName: J. Laura 32 | AuthorURL: https://astrogeology.usgs.gov 33 | - Title: "Querying for Data in an ROI and Loading it into QGIS" 34 | URL: https://stac.astrogeology.usgs.gov/docs/examples/to_qgis/ 35 | AuthorName: J. Laura 36 | AuthorURL: https://astrogeology.usgs.gov 37 | Tools & Applications: 38 | - Title: PySTAC Client 39 | URL: https://github.com/stac-utils/pystac-client 40 | AuthorName: PySTAC-Client Contributors 41 | AuthorURL: https://github.com/stac-utils/pystac-client/graphs/contributors 42 | -------------------------------------------------------------------------------- /datasets/nasa-usgs-mars-hirise.yaml: -------------------------------------------------------------------------------- 1 | Name: NASA / USGS Uncontrolled HiRISE RDRs 2 | Description: | 3 | These data are red and color Reduced Data Record (RDR) observations collected and originally processed by the High Resolution Imaging Science Experiment (HiRISE) team. The mdata are processed from the Planetary Data System (PDS) stored RDRs, map projected, and converted to Cloud Optimized GeoTiffs (COGs) for efficient remote data access. These data are not photogrammetrically controlled and use a priori NAIF SPICE pointing. Therefore, these data will not co-register with controlled data products. Data are released using simple cylindrical (planetocentric positive East, center longitude 0, -180 - 180 longitude domain) or a pole centered polar stereographic projection. Data are projected to the appropriate IAU Well-known Text v2 (WKT2) represented projection. 4 | Documentation: https://stac.astrogeology.usgs.gov/docs/data/mars/uncontrolled_hirise/ 5 | Contact: https://answers.usgs.gov/ 6 | ManagedBy: "[NASA](https://www.nasa.gov)" 7 | UpdateFrequency: HiRISE data will be updated as new releases are made to the Planetary Data System. 8 | Tags: 9 | - aws-pds 10 | - planetary 11 | - satellite imagery 12 | - stac 13 | - cog 14 | License: "[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)" 15 | Citation: https://doi.org/10.5066/P944DLP8 16 | Resources: 17 | - Description: Scenes and metadata 18 | ARN: arn:aws:s3:::astrogeo-ard/mars/mro/hirise/uncontrolled_rdr_observations/ 19 | Region: us-west-2 20 | Type: S3 Bucket 21 | Explore: 22 | - '[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/collections/mro_hirise_uncontrolled_observations)' 23 | DataAtWork: 24 | Tutorials: 25 | - Title: "Discovering and Downloading Data via the Command Line" 26 | URL: https://stac.astrogeology.usgs.gov/docs/tutorials/cli/ 27 | AuthorName: J. Laura 28 | AuthorURL: https://astrogeology.usgs.gov 29 | - Title: "Discovering and Downloading Data with Python" 30 | URL: https://stac.astrogeology.usgs.gov/docs/tutorials/basicpython/ 31 | AuthorName: J. Laura 32 | AuthorURL: https://astrogeology.usgs.gov 33 | - Title: "Querying for Data in an ROI and Loading it into QGIS" 34 | URL: https://stac.astrogeology.usgs.gov/docs/examples/to_qgis/ 35 | AuthorName: J. Laura 36 | AuthorURL: https://astrogeology.usgs.gov 37 | Tools & Applications: 38 | - Title: PySTAC Client 39 | URL: https://github.com/stac-utils/pystac-client 40 | AuthorName: PySTAC-Client Contributors 41 | AuthorURL: https://github.com/stac-utils/pystac-client/graphs/contributors 42 | -------------------------------------------------------------------------------- /datasets/nasa-usgs-themis-mosaics.yaml: -------------------------------------------------------------------------------- 1 | Name: NASA / USGS Controlled THEMIS Mosaics 2 | Description: | 3 | These data are infrared image mosaics, tiled to the Mars quadrangle, generated using Thermal Emission Imaging System (THEMIS) images from the 2001 Mars Odyssey orbiter mission. The mosaic is generated at the full resolution of the THEMIS infrared dataset, which is approximately 100 meters/pixel. The mosaic was absolutely photogrammetrically controlled to an improved Viking MDIM network that was develop by the USGS Astrogeology processing group using the Integrated Software for Imagers and Spectrometers. Image-to-image alignment precision is subpixel (i.e., <100m). These 8-bit, qualitative data are released as losslessly compressed Cloud Optimized GeoTiffs (COGs). Data are released using simple cylindrical (planetocentric positive East, center longitude 0, -180 to 180 longitude domain). 4 | Documentation: https://stac.astrogeology.usgs.gov/docs/data/mars/themis_controlled_mosaics/ 5 | Contact: https://answers.usgs.gov/ 6 | ManagedBy: "[NASA](https://www.nasa.gov)" 7 | UpdateFrequency: None planned. 8 | Tags: 9 | - aws-pds 10 | - planetary 11 | - satellite imagery 12 | - stac 13 | - cog 14 | License: "[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)" 15 | Citation: https://doi.org/10.5066/P96FA04Z 16 | Resources: 17 | - Description: Scenes and metadata 18 | ARN: arn:aws:s3:::astrogeo-ard/mars/mo/themis/controlled_mosaics/ 19 | Region: us-west-2 20 | Type: S3 Bucket 21 | Explore: 22 | - '[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/api/collections/mo_themis_controlled_mosaics)' 23 | DataAtWork: 24 | Tutorials: 25 | - Title: "Discovering and Downloading Data via the Command Line" 26 | URL: https://stac.astrogeology.usgs.gov/docs/tutorials/cli/ 27 | AuthorName: J. Laura 28 | AuthorURL: https://astrogeology.usgs.gov 29 | - Title: "Discovering and Downloading Data with Python" 30 | URL: https://stac.astrogeology.usgs.gov/docs/tutorials/basicpython/ 31 | AuthorName: J. Laura 32 | AuthorURL: https://astrogeology.usgs.gov 33 | - Title: "Querying for Data in an ROI and Loading it into QGIS" 34 | URL: https://stac.astrogeology.usgs.gov/docs/examples/to_qgis/ 35 | AuthorName: J. Laura 36 | AuthorURL: https://astrogeology.usgs.gov 37 | Tools & Applications: 38 | - Title: PySTAC Client 39 | URL: https://github.com/stac-utils/pystac-client 40 | AuthorName: PySTAC-Client Contributors 41 | AuthorURL: https://github.com/stac-utils/pystac-client/graphs/contributors 42 | -------------------------------------------------------------------------------- /datasets/nasa-usgs-themis-mosasics.yaml: -------------------------------------------------------------------------------- 1 | Name: NASA / USGS Controlled THEMIS Mosaics 2 | Description: | 3 | These data are infrared image mosaics, tiled to the Mars quadrangle, generated using Thermal Emission Imaging System (THEMIS) images from the 2001 Mars Odyssey orbiter mission. The mosaic is generated at the full resolution of the THEMIS infrared dataset, which is approximately 100 meters/pixel. The mosaic was absolutely photogrammetrically controlled to an improved Viking MDIM network that was develop by the USGS Astrogeology processing group using the Integrated Software for Imagers and Spectrometers. Image-to-image alignment precision is subpixel (i.e., <100m). These 8-bit, qualitative data are released as losslessly compressed Cloud Optimized GeoTiffs (COGs). Data are released using simple cylindrical (planetocentric positive East, center longitude 0, -180 to 180 longitude domain). 4 | Documentation: https://stac.astrogeology.usgs.gov/docs/data/mars/themis_controlled_mosaics/ 5 | Contact: https://answers.usgs.gov/ 6 | ManagedBy: "[NASA](https://www.nasa.gov)" 7 | UpdateFrequency: None planned. 8 | Tags: 9 | - aws-pds 10 | - planetary 11 | - satellite imagery 12 | - stac 13 | - cog 14 | License: "[CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/)" 15 | Citation: https://doi.org/10.5066/P96FA04Z 16 | Resources: 17 | - Description: Scenes and metadata 18 | ARN: arn:aws:s3:::astrogeo-ard/mars/mo/themis/controlled_mosaics/ 19 | Region: us-west-2 20 | Type: S3 Bucket 21 | Explore: 22 | - '[STAC Catalog](https://stac.astrogeology.usgs.gov/browser-dev/#/collections/mo_themis_controlled_mosaics)' 23 | DataAtWork: 24 | Tutorials: 25 | - Title: "Discovering and Downloading Data via the Command Line" 26 | URL: https://stac.astrogeology.usgs.gov/docs/tutorials/cli/ 27 | AuthorName: J. Laura 28 | AuthorURL: https://astrogeology.usgs.gov 29 | - Title: "Discovering and Downloading Data with Python" 30 | URL: https://stac.astrogeology.usgs.gov/docs/tutorials/basicpython/ 31 | AuthorName: J. Laura 32 | AuthorURL: https://astrogeology.usgs.gov 33 | - Title: "Querying for Data in an ROI and Loading it into QGIS" 34 | URL: https://stac.astrogeology.usgs.gov/docs/examples/to_qgis/ 35 | AuthorName: J. Laura 36 | AuthorURL: https://astrogeology.usgs.gov 37 | Tools & Applications: 38 | - Title: PySTAC Client 39 | URL: https://github.com/stac-utils/pystac-client 40 | AuthorName: PySTAC-Client Contributors 41 | AuthorURL: https://github.com/stac-utils/pystac-client/graphs/contributors 42 | -------------------------------------------------------------------------------- /datasets/noaa-coastal-lidar.yaml: -------------------------------------------------------------------------------- 1 | Name: NOAA Coastal Lidar Data 2 | Description: Lidar (light detection and ranging) is a technology that can measure the 3-dimentional location of objects, including the solid earth surface. The data consists of a point cloud of the positions of solid objects that reflected a laser pulse, typically from an airborne platform. In addition to the position, each point may also be attributed by the type of object it reflected from, the intensity of the reflection, and other system dependent metadata. The NOAA Coastal Lidar Data is a collection of lidar projects from many different sources and agencies, geographically focused on the coastal areas of the United States of America. The data is provided in Entwine Point Tiles (EPT; https://entwine.io) format, which is a lossless streamable octree of the point cloud, and in LAZ format. Datasets are maintained in their original projects and care should be taken when merging projects. The coordinate reference system for the data is The NAD83(2011) UTM zone appropriate for the center of each data set for EPT and geographic coordinates for LAZ. Vertically they are in the orthometric datum appropriate for that area (for example, NAVD88 in the mainland United States, PRVD02 in Puerto Rico, or GUVD03 in Guam). The geoid model used is reflected in the data set resource name.
The data are organized under directories entwine and laz for the EPT and LAZ versions respectively. Some datasets are not in EPT format, either because the dataset is already in EPT on the USGS public lidar site, they failed to build or their content does not work well in EPT format. Topobathy lidar datasets using the topobathy domain profile do not translate well to EPT format. 3 | Documentation: https://coast.noaa.gov/digitalcoast/data/coastallidar.html and https://coast.noaa.gov/digitalcoast/data/jalbtcx.html. A table providing the dataset names and links is at https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/index.html 4 | Contact: For any questions regarding data delivery or any general questions regarding the NOAA Open Data Dissemination (NODD) Program, email the NODD Team at nodd@noaa.gov. For general questions or feedback about the data, please submit inquiries through the NOAA Office for Coastal Management Contact Form https://coast.noaa.gov/contactform/. 5 |
We also seek to identify case studies on how NOAA data is being used and will be featuring those stories in joint publications and in upcoming events. If you are interested in seeing your story highlighted, please share it with the NODD team by emailing nodd@noaa.gov 6 | ManagedBy: "[NOAA](https://www.noaa.gov/)" 7 | UpdateFrequency: Periodically, as new data becomes available 8 | Collabs: 9 | ASDI: 10 | Tags: 11 | - elevation 12 | Tags: 13 | - aws-pds 14 | - climate 15 | - elevation 16 | - disaster response 17 | - geospatial 18 | - lidar 19 | - stac 20 | License: NOAA data disseminated through NODD are open to the public and can be used as desired. 21 |
22 |
23 | NOAA makes data openly available to ensure maximum use of our data, and to spur and encourage exploration and innovation throughout the industry. NOAA requests attribution for the use or dissemination of unaltered NOAA data. However, it is not permissible to state or imply endorsement by or affiliation with NOAA. If you modify NOAA data, you may not state or imply that it is original, unaltered NOAA data. 24 | Resources: 25 | - Description: NOAA Coastal Lidar Dataset 26 | ARN: arn:aws:s3:::noaa-nos-coastal-lidar-pds 27 | Region: us-east-1 28 | Type: S3 Bucket 29 | Explore: 30 | - '[STAC V1.0.0 endpoint](https://noaa-nos-coastal-lidar-pds.s3.us-east-1.amazonaws.com/entwine/stac/catalog.json)' 31 | - Description: NOAA Coastal Lidar Dataset New Dataset Notification 32 | ARN: arn:aws:sns:us-east-1:709902155096:NewCoastalLidarObject 33 | Region: us-east-1 34 | Type: SNS Topic 35 | DataAtWork: 36 | Tools & Applications: 37 | - Title: OpenTopography access and processing of NOAA Coastal Lidar Data 38 | URL: https://portal.opentopography.org/datasets 39 | AuthorName: OpenTopography 40 | AuthorURL: https://opentopography.org/ 41 | -------------------------------------------------------------------------------- /datasets/nz-elevation.yaml: -------------------------------------------------------------------------------- 1 | Name: New Zealand Elevation 2 | Description: | 3 | The New Zealand Elevation dataset consists of New Zealand's publicly owned digital elevation models and digital surface models, which are freely available to use under an open licence. The dataset contains 1m resolution grids derived from LiDAR data. Point clouds are not included in the initial release. 4 | 5 | All of the elevation files are [Cloud Optimised GeoTIFFs](https://www.cogeo.org/) using LERC compression for the main grid and LERC compression with lower max_z_error for the overviews. These elevation files are accompanied by [STAC metadata](https://stacspec.org/). The elevation data is organised by region and survey. 6 | Documentation: https://github.com/linz/elevation 7 | Contact: elevation@linz.govt.nz 8 | ManagedBy: "[Toitū Te Whenua Land Information New Zealand](https://www.linz.govt.nz)" 9 | UpdateFrequency: New elevation data will regularly be added, as part of being published to the LINZ Data Service and LINZ Basemaps. 10 | 11 | Tags: 12 | - aws-pds 13 | - elevation 14 | - earth observation 15 | - stac 16 | - cog 17 | - geospatial 18 | 19 | License: Toitū Te Whenua Land Information New Zealand does not own the copyright for all of the elevation data that is available in this bucket. Licensors are contained in the STAC Collection file accompanying each elevation data survey. Licensed for re-use under "[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)". 20 | Citation: Licensed by (insert the licensor) for re-use under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). 21 | 22 | Resources: 23 | - Description: New Zealand Elevation with accompanying metadata 24 | ARN: arn:aws:s3:::nz-elevation 25 | Region: ap-southeast-2 26 | Type: S3 Bucket 27 | 28 | DataAtWork: 29 | Tutorials: 30 | - Title: Browsing the s3://nz-elevation bucket 31 | URL: https://github.com/linz/elevation/blob/master/docs/usage.md 32 | AuthorName: Toitū Te Whenua Land Information New Zealand 33 | AuthorURL: https://www.linz.govt.nz 34 | 35 | - Title: Elevation survey naming and path conventions 36 | URL: https://github.com/linz/elevation/blob/master/docs/naming.md 37 | AuthorName: Toitū Te Whenua Land Information New Zealand 38 | AuthorURL: https://www.linz.govt.nz 39 | 40 | Tools & Applications: 41 | - Title: LINZ Basemaps 42 | URL: https://basemaps.linz.govt.nz 43 | AuthorName: Toitū Te Whenua Land Information New Zealand 44 | AuthorURL: https://www.linz.govt.nz 45 | 46 | - Title: LINZ Data Service 47 | URL: https://data.linz.govt.nz 48 | AuthorName: Toitū Te Whenua Land Information New Zealand 49 | AuthorURL: https://www.linz.govt.nz 50 | 51 | - Title: LINZ Topographic Workflows - Bulk elevation data processing with Kubernetes and Argo Workflows 52 | URL: https://github.com/linz/topo-workflows 53 | AuthorName: Toitū Te Whenua Land Information New Zealand 54 | AuthorURL: https://www.linz.govt.nz 55 | 56 | Publications: 57 | - Title: Stress Detection in New Zealand Kauri Canopies with WorldView-2 Satellite and LiDAR Data 58 | URL: https://doi.org/10.3390/rs12121906 59 | AuthorName: Jane J. Meiforth, Henning Buddenbaum, Joachim Hill, James D. Shepherd and John R. Dymond 60 | 61 | - Title: Dairy farming exposure and impacts from coastal flooding and sea level rise in Aotearoa-New Zealand 62 | URL: https://doi.org/10.1016/j.ijdrr.2023.104079 63 | AuthorName: Heather Craig, Alec Wild and Ryan Paulik 64 | 65 | - Title: "Underpinning Terroir with Data: Integrating Vineyard Performance Metrics with Soil and Climate Data to Better Understand Within-Region Variation in Marlborough, New Zealand" 66 | URL: https://doi.org/10.1155/2023/8811402 67 | AuthorName: R. G. V. Bramley, J. Ouzman, A. P. Sturman, G. J. Grealish, C. E. M. Ratcliff and M. C. T. Trought 68 | -------------------------------------------------------------------------------- /datasets/nz-imagery.yaml: -------------------------------------------------------------------------------- 1 | Name: New Zealand Imagery 2 | Description: | 3 | The New Zealand Imagery dataset consists of New Zealand's publicly owned aerial and satellite imagery, which is freely available to use under an open licence. The dataset ranges from the latest high-resolution aerial imagery down to 5cm in some urban areas to lower resolution satellite imagery that provides full coverage of mainland New Zealand, Chathams and other offshore islands. It also includes historical imagery that has been scanned from film, orthorectified (removing distortions) and georeferenced (correctly positioned) to create a unique and crucial record of changes to the New Zealand landscape.
4 | All of the imagery files are [Cloud Optimised GeoTIFFs](https://www.cogeo.org/) using lossless WEBP compression for the main image and lossy WEBP compression for the overviews. These image files are accompanied by [STAC metadata](https://stacspec.org/). The imagery is organised by region and survey. 5 | Documentation: https://github.com/linz/imagery 6 | Contact: imagery@linz.govt.nz 7 | ManagedBy: "[Toitū Te Whenua Land Information New Zealand](https://www.linz.govt.nz)" 8 | UpdateFrequency: New imagery will regularly be added, as part of being published to the LINZ Data Service and LINZ Basemaps. 9 | 10 | Tags: 11 | - aws-pds 12 | - aerial imagery 13 | - satellite imagery 14 | - earth observation 15 | - stac 16 | - cog 17 | - geospatial 18 | 19 | License: Toitū Te Whenua Land Information New Zealand does not own the copyright for all of the imagery that is available in this bucket. Licensors are contained in the STAC Collection file accompanying each imagery survey. Licensed for re-use under "[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)". 20 | Citation: Licensed by (insert the licensor) for re-use under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). 21 | 22 | Resources: 23 | - Description: New Zealand Imagery with accompanying metadata 24 | ARN: arn:aws:s3:::nz-imagery 25 | Region: ap-southeast-2 26 | Type: S3 Bucket 27 | 28 | DataAtWork: 29 | Tutorials: 30 | - Title: Browsing the s3://nz-imagery bucket 31 | URL: https://github.com/linz/imagery/blob/master/docs/usage.md 32 | AuthorName: Toitū Te Whenua Land Information New Zealand 33 | AuthorURL: https://www.linz.govt.nz 34 | 35 | - Title: Imagery survey naming and path conventions 36 | URL: https://github.com/linz/imagery/blob/master/docs/naming.md 37 | AuthorName: Toitū Te Whenua Land Information New Zealand 38 | AuthorURL: https://www.linz.govt.nz 39 | 40 | - Title: "ArcGIS Workflows: NZ Imagery from a public AWS S3 bucket" 41 | URL: https://storymaps.arcgis.com/collections/e6d212054d9744f399fcbed00a75ee43 42 | AuthorName: Eagle Technology 43 | AuthorURL: https://www.eagle.co.nz/ 44 | 45 | Tools & Applications: 46 | - Title: LINZ Basemaps 47 | URL: https://basemaps.linz.govt.nz 48 | AuthorName: Toitū Te Whenua Land Information New Zealand 49 | AuthorURL: https://www.linz.govt.nz 50 | 51 | - Title: LINZ Data Service 52 | URL: https://data.linz.govt.nz 53 | AuthorName: Toitū Te Whenua Land Information New Zealand 54 | AuthorURL: https://www.linz.govt.nz 55 | 56 | - Title: LINZ Topographic Workflows - Bulk imagery processing with Kubernetes and Argo Workflows 57 | URL: https://github.com/linz/topo-workflows 58 | AuthorName: Toitū Te Whenua Land Information New Zealand 59 | AuthorURL: https://www.linz.govt.nz 60 | 61 | Publications: 62 | - Title: A Boundary Regulated Network for Accurate Roof Segmentation and Outline Extraction 63 | URL: https://doi.org/10.3390/rs10081195 64 | AuthorName: Guangming Wu, Zhiling Guo, Xiaodan Shi, Qi Chen, Yongwei Xu, Ryosuke Shibasaki and Xiaowei Shao 65 | 66 | - Title: Semantic Segmentation of Urban Buildings from VHR Remote Sensing Imagery Using a Deep Convolutional Neural Network 67 | URL: https://doi.org/10.3390/rs11151774 68 | AuthorName: Yaning Yi, Zhijie Zhang, Wanchang Zhang, Chuanrong Zhang, Weidong Li and Tian Zhao 69 | 70 | - Title: Deep Learning and Phenology Enhance Large-Scale Tree Species Classification in Aerial Imagery during a Biosecurity Response 71 | URL: https://doi.org/10.3390/rs13091789 72 | AuthorName: Grant D. Pearse, Michael S. Watt, Julia Soewarto and Alan Y. S. Tan 73 | 74 | - Title: A Large Contextual Dataset for Classification, Detection and Counting of Cars with Deep Learning 75 | URL: https://doi.org/10.1007/978-3-319-46487-9_48 76 | AuthorName: T. Nathan Mundhenk, Goran Konjevod, Wesam A. Sakla & Kofi Boakye 77 | -------------------------------------------------------------------------------- /datasets/palsar-2-scansar-flooding-in-bangladesh.yaml: -------------------------------------------------------------------------------- 1 | Name: PALSAR-2 ScanSAR Tropical Cycolne Mocha (L2.1) 2 | Description: Tropical Cyclone Mocha began to form in the Bay of Bengal on 11 May 2023 and continues to intensify as it moves towards Myanmar and Bangladesh.Cyclone Mocha is the first storm to form in the Bay of Bengal this year and is expected to hit several coastal areas in Bangladesh on 14 May with wind speeds of up to 175 km/h.After made its landfall in the coast between Cox’s Bazar (Bangladesh) and Kyaukphyu (Myanmar) near Sittwe (Myanmar). At most, Catastrophic Damage-causing winds was possible especially in the areas of Rakhine State and Chin State, and Severe Damage-causing winds is possible in the areas of Rakhine, Chin, Magway, and Sagaing ([source] TAOS Model, DisasterAWARE). Bangladesh were preparing to evacuate over 500,000 people as the World Meteorological Organisation (WMO) has warned of big humanitarian impacts once the storm makes landfall. Due to its intensity, Cyclone Mocha is expected to inundate several low-lying areas of the delta nation of Bangladesh which could consequently cause landslides.576 cyclone shelters are ready to provide refuge to those evacuated however damage to infrastructure and agriculture would be devastating.Myanmar ? POTENTIAL OF A CATASTROPHIC DISASTER. An estimated 8.7 Million people, 1.9M households, and $35.3 Billion (USD) of infrastructure (total replacement value) were potentially exposed to moderate to severe damaging winds in accordance with AHA Centre.JAXA has responded to the Tropical Cyclone MOCHA by conducting emergency disaster observations and providing data as requested through the International Disaster Charter and Sentinel Asia. The 25 m PALSAR-2 ScanSAR is normalized backscatter data of PALSAR-2 broad area observation mode with observation width of 350 km. Polarization data are stored as 16-bit digital numbers (DN). The DN values can be converted to gamma naught values in decibel unit (dB) using the following equation sigma zero = 10*log10(DN2) - 83.0 dB. Included in this dataset are ALOS-2 PALSAR-2 ScanSAR 2.1 data. Level 2.1 data is orthorectified from level 1.1 data by using digital elevation model. Pixel spacing is selectable depending on observation modes. Image coordinate in map projection is geocoded. 3 | UpdateFrequency: As available. 4 | License: Data is available for free under the terms of use. 5 | Documentation: https://www.eorc.jaxa.jp/ALOS/en/dataset/alos_open_and_free_e.htm, https://www.eorc.jaxa.jp/ALOS/en/dataset/palsar2_l22_e.htm 6 | ManagedBy: "[JAXA](https://www.jaxa.jp/)" 7 | Contact: aproject@jaxa.jp 8 | Tags: 9 | - aws-pds 10 | - agriculture 11 | - cog 12 | - disaster response 13 | - earth observation 14 | - geospatial 15 | - natural resource 16 | - satellite imagery 17 | - stac 18 | - sustainability 19 | - synthetic aperture radar 20 | DataAtWork: 21 | Tutorials: 22 | Tools & Applications: 23 | Publications: 24 | - Title: "ALOS series Open and Free Data by JAXA EORC" 25 | URL: https://www.eorc.jaxa.jp/ALOS/en/dataset/alos_open_and_free_e.htm 26 | AuthorName: JAXA EORC 27 | Resources: 28 | - Description: PALSAR-2 ScanSAR L2.2 29 | ARN: arn:aws:s3:::jaxaalos2/palsar2-scansar/Bangladesh/ 30 | Region: us-west-2 31 | Type: S3 Bucket 32 | RequesterPays: False 33 | -------------------------------------------------------------------------------- /datasets/palsar-2-scansar-flooding-in-rwanda.yaml: -------------------------------------------------------------------------------- 1 | Name: PALSAR-2 ScanSAR Flooding in Rwanda (L2.1) 2 | Description: Torrential rainfall triggered flooding and landslides in many parts of Rwanda. The hardest-hit districts were Ngororero, Rubavu, Nyabihu, Rutsiro and Karongi. According to reports, 14 people have died in Karongi, 26 in Rutsiro, 18 in Rubavu, 19 in Nyabihu and 18 in Ngororero.Rwanda National Police reported that the Mukamira-Ngororero and Rubavu-Rutsiro roads are impassable due to flooding and landslide debris. UNITAR on behalf of United Nations Office for the Coordination of Humanitarian Affairs (OCHA) / Regional Office for Southern & Eastern Africa in cooperation with Rwanda Space Agency (RSA) was activated International Disaster Charter. JAXA has responded to the flood event in Rwanda by conducting emergency disaster observations and providing data as requested by OCHA and RSA through the International Disaster Charter. The 25 m PALSAR-2 ScanSAR is normalized backscatter data of PALSAR-2 broad area observation mode with observation width of 350 km. Polarization data are stored as 16-bit digital numbers (DN). The DN values can be converted to gamma naught values in decibel unit (dB) using the following equation sigma zero = 10*log10(DN2) - 83.0 dB. Included in this dataset are ALOS-2 PALSAR-2 ScanSAR 2.1 data. Level 2.1 data is orthorectified from level 1.1 data by using digital elevation model. Pixel spacing is selectable depending on observation modes. Image coordinate in map projection is geocoded. 3 | UpdateFrequency: As available. 4 | License: Data is available for free under the terms of use. 5 | Documentation: https://www.eorc.jaxa.jp/ALOS/en/dataset/alos_open_and_free_e.htm, https://www.eorc.jaxa.jp/ALOS/en/dataset/palsar2_l22_e.htm 6 | ManagedBy: "[JAXA](https://www.jaxa.jp/)" 7 | Contact: aproject@jaxa.jp 8 | Tags: 9 | - aws-pds 10 | - agriculture 11 | - cog 12 | - deafrica 13 | - disaster response 14 | - earth observation 15 | - geospatial 16 | - natural resource 17 | - satellite imagery 18 | - stac 19 | - sustainability 20 | - synthetic aperture radar 21 | DataAtWork: 22 | Tutorials: 23 | Tools & Applications: 24 | Publications: 25 | - Title: "ALOS series Open and Free Data by JAXA EORC" 26 | URL: https://www.eorc.jaxa.jp/ALOS/en/dataset/alos_open_and_free_e.htm 27 | AuthorName: JAXA EORC 28 | Resources: 29 | - Description: PALSAR-2 ScanSAR L1.1 & L2.2 30 | ARN: arn:aws:s3:::jaxaalos2/palsar2-scansar/Rwanda/ 31 | Region: us-west-2 32 | Type: S3 Bucket 33 | RequesterPays: False 34 | -------------------------------------------------------------------------------- /datasets/palsar2-scansar-turkey-syria.yaml: -------------------------------------------------------------------------------- 1 | Name: PALSAR-2 ScanSAR Turkey & Syria Earthquake (L2.1 & L1.1) 2 | Description: | 3 | JAXA has responded to the Earthquake events in Turkey and Syria by conducting emergency disaster observations and providing data as requested by the Disaster and Emergency Management Authority (AFAD), Ministry of Interior in Turkey, through Sentinel Asia and the International Disaster Charter. Additional information on the event and dataset can be found [here](https://earth.jaxa.jp/en/earthview/2023/02/14/7381/index.html). The 25 m PALSAR-2 ScanSAR is normalized backscatter data of PALSAR-2 broad area observation mode with observation width of 350 km. Polarization data are stored as 16-bit digital numbers (DN). The DN values can be converted to gamma naught values in decibel unit (dB) using the following equation: γ0 = 10*log10(DN2) - 83.0 dB. Included in this dataset are ALOS PALSAR Level 1.1 and 2.1 data. Level 1.1 is range and single look azimuth compressed data represented by complex I and Q channels to preserve the magnitude and phase information. Range coordinate is in slant range. In the case of ScanSAR mode, an image file is generated per each scan. Level 2.1 data is orthorectified from level 1.1 data by using digital elevation model. Pixel spacing is selectable depending on observation modes. Image coordinate in map projection is geocoded. 4 | Documentation: https://www.eorc.jaxa.jp/ALOS/en/dataset/alos_open_and_free_e.htm, https://www.eorc.jaxa.jp/ALOS/en/dataset/palsar2_l22_e.htm 5 | Contact: aproject@jaxa.jp 6 | ManagedBy: "[JAXA](https://www.jaxa.jp/)" 7 | UpdateFrequency: As available. 8 | Collabs: 9 | ASDI: 10 | Tags: 11 | - satellite imagery 12 | Tags: 13 | - aws-pds 14 | - agriculture 15 | - earth observation 16 | - satellite imagery 17 | - geospatial 18 | - natural resource 19 | - sustainability 20 | - disaster response 21 | - synthetic aperture radar 22 | - deafrica 23 | - stac 24 | - cog 25 | License: Data is available for free under the [terms of use](https://earth.jaxa.jp/policy/en.html). 26 | Resources: 27 | - Description: PALSAR-2 ScanSAR L1.1 & L2.2 28 | ARN: arn:aws:s3:::jaxaalos2/palsar2-scansar/Turkey-Syria-earthquake/ 29 | Region: us-west-2 30 | Type: S3 Bucket 31 | RequesterPays: False 32 | DataAtWork: 33 | Tutorials: 34 | Tools & Applications: 35 | Publications: 36 | - Title: "ALOS series Open and Free Data" 37 | URL: https://www.eorc.jaxa.jp/ALOS/en/dataset/alos_open_and_free_e.htm 38 | AuthorName: JAXA EORC 39 | - Title: "ALOS-2 observations of earthquakes in southeastern Turkey in 2023" 40 | URL: https://earth.jaxa.jp/en/earthview/2023/02/14/7381/index.html 41 | AuthorName: JAXA EORC 42 | 43 | -------------------------------------------------------------------------------- /datasets/pgc-arcticdem.yaml: -------------------------------------------------------------------------------- 1 | Name: ArcticDEM 2 | Description: ArcticDEM - 2m GSD Digital Elevation Models (DEMs) and mosaics from 2007 to the present. The ArcticDEM project seeks to fill the need for high-resolution time-series elevation data in the Arctic. The time-dependent nature of the strip DEM files allows users to perform change detection analysis and to compare observations of topography data acquired in different seasons or years. The mosaic DEM tiles are assembled from multiple strip DEMs with the intention of providing a more consistent and comprehensive product over large areas. ArcticDEM data is constructed from in-track and cross-track high-resolution (~0.5 meter) imagery acquired by the Maxar constellation of optical imaging satellites. 3 | Documentation: https://www.pgc.umn.edu/data/arcticdem/ 4 | Contact: pgc-support@umn.edu 5 | ManagedBy: "[Polar Geospatial Center](https://www.pgc.umn.edu/)" 6 | UpdateFrequency: New DEM strips are added twice yearly. Mosaic products are added as soon as they are available. 7 | Collabs: 8 | ASDI: 9 | Tags: 10 | - satellite imagery 11 | Tags: 12 | - aws-pds 13 | - elevation 14 | - earth observation 15 | - geospatial 16 | - mapping 17 | - open source software 18 | - satellite imagery 19 | - cog 20 | - stac 21 | License: "[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)" 22 | Citation: "See PGC's [acknowledgement policy](https://www.pgc.umn.edu/guides/user-services/acknowledgement-policy/)" 23 | Resources: 24 | - Description: ArcticDEM DEM Mosaics 25 | ARN: arn:aws:s3:::pgc-opendata-dems/arcticdem/mosaics/ 26 | Region: us-west-2 27 | Type: S3 Bucket 28 | Explore: 29 | - '[Browse Static STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/arcticdem/mosaics.json)' 30 | - '[Dynamic STAC API Endpoint](https://stac.pgc.umn.edu/api/v1/)' 31 | - Description: ArcticDEM DEM Strips 32 | ARN: arn:aws:s3:::pgc-opendata-dems/arcticdem/strips/ 33 | Region: us-west-2 34 | Type: S3 Bucket 35 | Explore: 36 | - '[Browse Static STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/arcticdem/strips.json)' 37 | - '[Dynamic STAC API Guide](https://www.pgc.umn.edu/guides/stereo-derived-elevation-models/stac-access-static-and-dynamic-api/)' 38 | DataAtWork: 39 | Tutorials: 40 | - Title: PGC Dynamic STAC API Tutorial 41 | URL: https://polargeospatialcenter.github.io/pgc-code-tutorials/dynamic_stac_api/web_files/stac_api_demo_workflow.html 42 | AuthorName: Polar Geospatial Center 43 | Tools & Applications: 44 | - Title: ArcticDEM Explorer 45 | URL: https://arcticdem.apps.pgc.umn.edu/ 46 | AuthorName: Polar Geospatial Center & ESRI 47 | - Title: OpenTopography access to ArcticDEM 48 | URL: https://doi.org/10.5069/G96Q1VFK 49 | AuthorName: OpenTopography 50 | AuthorURL: https://opentopography.org/ 51 | Publications: 52 | - Title: "The surface extraction from TIN based search-space minimization (SETSM) algorithm" 53 | URL: https://doi.org/10.1016/j.isprsjprs.2017.04.019 54 | AuthorName: Myoung-Jong Noh, Ian M. Howat 55 | - Title: "Automated stereo-photogrammetric DEM generation at high latitudes: Surface Extraction with TIN-based Search-space Minimization (SETSM) validation and demonstration over glaciated regions" 56 | URL: https://doi.org/10.1080/15481603.2015.1008621 57 | AuthorName: Myoung-Jong Noh, Ian M. Howat 58 | - Title: "Automatic relative RPC image model bias compensation through hierarchical image matching for improving DEM quality" 59 | URL: https://doi.org/10.1016/j.isprsjprs.2017.12.008 60 | AuthorName: Myoung-Jong Noh, Ian M. Howat 61 | - Title: "Dynamic ice loss from the Greenland Ice Sheet driven by sustained glacier retreat" 62 | URL: https://doi.org/10.1038/s43247-020-0001-2 63 | AuthorName: Michalea D. King, Ian M. Howat, Salvatore G. Candela, Myoung J. Noh, Seongsu Jeong, Brice P. Y. Noël, Michiel R. van den Broeke, Bert Wouters, Adelaide Negrete 64 | - Title: "Future Evolution of Greenland's Marine-Terminating Outlet Glaciers" 65 | URL: https://doi.org/10.1029/2018JF004873 66 | AuthorName: Ginny A. Catania, Leigh A. Stearns, Twila A. Moon, Ellen M. Enderlin, R. H. Jackson 67 | -------------------------------------------------------------------------------- /datasets/pgc-earthdem.yaml: -------------------------------------------------------------------------------- 1 | Name: EarthDEM 2 | Description: EarthDEM - 2m GSD Digital Elevation Models (DEMs) and mosaics from 2002 to the present. The EarthDEM project seeks to fill the need for high-resolution time-series elevation data in non-polar regions. The time-dependent nature of the strip DEM files allows users to perform change detection analysis and to compare observations of topography data acquired in different seasons or years. The mosaic DEM tiles are assembled from multiple strip DEMs with the intention of providing a more consistent and comprehensive product over large areas. EarthDEM data is constructed from in-track and cross-track high-resolution (~0.5 meter) imagery acquired by the Maxar constellation of optical imaging satellites. Only a portion of the worldwide EarthDEM dataset is available publicly. Federally-funded researchers may access the entire dataset via NASA CSDA's Smallsat Data Explorer (see Data at Work section). 3 | Documentation: https://www.pgc.umn.edu/data/earthdem/ 4 | Contact: pgc-support@umn.edu 5 | ManagedBy: "[Polar Geospatial Center](https://www.pgc.umn.edu/)" 6 | UpdateFrequency: New DEM strips are added when allowed by licensing restrictions. Mosaic products are added as soon as they are available. 7 | Collabs: 8 | ASDI: 9 | Tags: 10 | - satellite imagery 11 | Tags: 12 | - aws-pds 13 | - elevation 14 | - earth observation 15 | - geospatial 16 | - mapping 17 | - open source software 18 | - satellite imagery 19 | - cog 20 | - stac 21 | License: "[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)" 22 | Citation: "See PGC's [acknowledgement policy](https://www.pgc.umn.edu/guides/user-services/acknowledgement-policy/)" 23 | Resources: 24 | - Description: EarthDEM DEM Strips 25 | ARN: arn:aws:s3:::pgc-opendata-dems/earthdem/strips/ 26 | Region: us-west-2 27 | Type: S3 Bucket 28 | Explore: 29 | - '[Browse Static STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/earthdem/strips.json)' 30 | - '[Dynamic STAC API Guide](https://www.pgc.umn.edu/guides/stereo-derived-elevation-models/stac-access-static-and-dynamic-api/)' 31 | DataAtWork: 32 | Tutorials: 33 | - Title: PGC Dynamic STAC API Tutorial 34 | URL: https://polargeospatialcenter.github.io/pgc-code-tutorials/dynamic_stac_api/web_files/stac_api_demo_workflow.html 35 | AuthorName: Polar Geospatial Center 36 | Tools & Applications: 37 | - Title: GLARS Data Viewer 38 | URL: https://glars.org/data/view-and-download/ 39 | AuthorName: Great Lakes Alliance for Remote Sensing 40 | - Title: NASA CSDA SmallSat Data Explorer 41 | URL: https://csdap.earthdata.nasa.gov/ 42 | AuthorName: NASA Commercial Smallsat Acquisition Program 43 | Publications: 44 | - Title: "Multi-Source EO for Dynamic Wetland Mapping and Monitoring in the Great Lakes Basin" 45 | URL: https://doi.org/10.3390/rs13040599 46 | AuthorName: Michael J. Battaglia, Sarah Banks, Amir Behnamian, Laura Bourgeau-Chavez, Brian Brisco, Jennifer Corcoran, Zhaohua Chen, Brian Huberty, James Klassen, Joseph Knight, Paul Morin, Kevin Murnaghan, Keith Pelletier, Lori White 47 | - Title: "The surface extraction from TIN based search-space minimization (SETSM) algorithm" 48 | URL: https://doi.org/10.1016/j.isprsjprs.2017.04.019 49 | AuthorName: Myoung-Jong Noh, Ian M. Howat 50 | - Title: "Automated stereo-photogrammetric DEM generation at high latitudes: Surface Extraction with TIN-based Search-space Minimization (SETSM) validation and demonstration over glaciated regions" 51 | URL: https://doi.org/10.1080/15481603.2015.1008621 52 | AuthorName: Myoung-Jong Noh, Ian M. Howat 53 | -------------------------------------------------------------------------------- /datasets/pgc-rema.yaml: -------------------------------------------------------------------------------- 1 | Name: Reference Elevation Model of Antarctica (REMA) 2 | Description: The Reference Elevation Model of Antarctica - 2m GSD Digital Elevation Models (DEMs) and mosaics from 2009 to the present. The REMA project seeks to fill the need for high-resolution time-series elevation data in the Antarctic. The time-dependent nature of the strip DEM files allows users to perform change detection analysis and to compare observations of topography data acquired in different seasons or years. The mosaic DEM tiles are assembled from multiple strip DEMs with the intention of providing a more consistent and comprehensive product over large areas. REMA data is constructed from in-track and cross-track high-resolution (~0.5 meter) imagery acquired by the Maxar constellation of optical imaging satellites. 3 | Documentation: https://www.pgc.umn.edu/data/rema/ 4 | Contact: pgc-support@umn.edu 5 | ManagedBy: "[Polar Geospatial Center](https://www.pgc.umn.edu/)" 6 | UpdateFrequency: New DEM strips are added twice yearly. Mosaic products are added as soon as they are available. 7 | Collabs: 8 | ASDI: 9 | Tags: 10 | - satellite imagery 11 | Tags: 12 | - aws-pds 13 | - elevation 14 | - earth observation 15 | - geospatial 16 | - mapping 17 | - open source software 18 | - satellite imagery 19 | - cog 20 | - stac 21 | License: "[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)" 22 | Citation: "See PGC's [acknowledgement policy](https://www.pgc.umn.edu/guides/user-services/acknowledgement-policy/)" 23 | Resources: 24 | - Description: REMA DEM Mosaics 25 | ARN: arn:aws:s3:::pgc-opendata-dems/rema/mosaics/ 26 | Region: us-west-2 27 | Type: S3 Bucket 28 | Explore: 29 | - '[Browse Static STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/rema/mosaics.json)' 30 | - '[Dynamic STAC API Endpoint](https://stac.pgc.umn.edu/api/v1/)' 31 | - Description: REMA DEM Strips 32 | ARN: arn:aws:s3:::pgc-opendata-dems/rema/strips/ 33 | Region: us-west-2 34 | Type: S3 Bucket 35 | Explore: 36 | - '[Browse Static STAC Catalog](https://polargeospatialcenter.github.io/stac-browser/#/external/pgc-opendata-dems.s3.us-west-2.amazonaws.com/rema/strips.json)' 37 | - '[Dynamic STAC API Guide](https://www.pgc.umn.edu/guides/stereo-derived-elevation-models/stac-access-static-and-dynamic-api/)' 38 | DataAtWork: 39 | Tutorials: 40 | - Title: PGC Dynamic STAC API Tutorial 41 | URL: https://polargeospatialcenter.github.io/pgc-code-tutorials/dynamic_stac_api/web_files/stac_api_demo_workflow.html 42 | AuthorName: Polar Geospatial Center 43 | Tools & Applications: 44 | - Title: REMA Explorer 45 | URL: https://rema.apps.pgc.umn.edu/ 46 | AuthorName: Polar Geospatial Center & ESRI 47 | - Title: OpenTopography access to REMA 48 | URL: https://doi.org/10.5069/G9X63K5S 49 | AuthorName: OpenTopography 50 | AuthorURL: https://opentopography.org/ 51 | Publications: 52 | - Title: "The Reference Elevation Model of Antarctica" 53 | URL: https://doi.org/10.5194/tc-13-665-2019 54 | AuthorName: Ian M. Howat, Claire Porter, Benjanim E. Smith, Myoung-Jong Noh, Paul Morin 55 | - Title: "The surface extraction from TIN based search-space minimization (SETSM) algorithm" 56 | URL: https://doi.org/10.1016/j.isprsjprs.2017.04.019 57 | AuthorName: Myoung-Jong Noh, Ian M. Howat 58 | - Title: "Automated stereo-photogrammetric DEM generation at high latitudes: Surface Extraction with TIN-based Search-space Minimization (SETSM) validation and demonstration over glaciated regions" 59 | URL: https://doi.org/10.1080/15481603.2015.1008621 60 | AuthorName: Myoung-Jong Noh, Ian M. Howat 61 | - Title: "Automatic relative RPC image model bias compensation through hierarchical image matching for improving DEM quality" 62 | URL: https://doi.org/10.1016/j.isprsjprs.2017.12.008 63 | AuthorName: Myoung-Jong Noh, Ian M. Howat 64 | - Title: "Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet" 65 | URL: https://doi.org/10.1038/s41561-019-0510-8 66 | AuthorName: Morlighem, M., Rignot, E., Binder, T. et al. 67 | -------------------------------------------------------------------------------- /datasets/radiant-mlhub.yaml: -------------------------------------------------------------------------------- 1 | Name: Radiant MLHub 2 | Description: Radiant MLHub is an open library for geospatial training data that hosts datasets generated by [Radiant Earth Foundation](https://www.radiant.earth/)'s team as well as other training data catalogs contributed by Radiant Earth’s partners. Radiant MLHub is open to anyone to access, store, register and/or share their training datasets for high-quality Earth observations. All of the training datasets are stored using a [SpatioTemporal Asset Catalog (STAC)](https://stacspec.org/) compliant catalog and exposed through a common API. Training datasets include pairs of imagery and labels for different types of machine learning problems including image classification, object detection, and semantic segmentation. Labels are generated from ground reference data and/or image annotation. 3 | Documentation: http://docs.mlhub.earth/ 4 | Contact: support@radiant.earth 5 | ManagedBy: "[Radiant Earth Foundation](https://www.radiant.earth/)" 6 | UpdateFrequency: New training data catalogs are added on a rolling basis 7 | Tags: 8 | - aws-pds 9 | - labeled 10 | - machine learning 11 | - geospatial 12 | - earth observation 13 | - satellite imagery 14 | - environmental 15 | - cog 16 | - stac 17 | License: Access to Radiant MLHub data is free for everyone. Each dataset has its own license (usually CC-BY or CC-BY-SA). View [Terms of Service](https://www.radiant.earth/terms/). 18 | Resources: 19 | - Description: Radiant MLHub Training Data 20 | ARN: arn:aws:s3:::radiant-mlhub 21 | Region: us-west-2 22 | Type: S3 Bucket 23 | DataAtWork: 24 | Tutorials: 25 | - Title: How to access Radiant MLHub Data 26 | URL: https://github.com/radiantearth/mlhub-tutorials/blob/master/RadiantMLHub-intro.pdf 27 | AuthorName: Radiant Earth 28 | AuthorURL: https://www.radiant.earth/ 29 | - Title: Radiant MLHub Tutorials with Jupyter Notebooks 30 | URL: https://github.com/radiantearth/mlhub-tutorials/tree/master/notebooks 31 | AuthorName: Kevin Booth 32 | AuthorURL: https://www.linkedin.com/in/kbgg/ 33 | - Title: "Explore wind speed images from Amazon Sustainability Data Initiative (ASDI) hosted on S3 using SageMaker Studio Lab (SMSL)" 34 | URL: https://github.com/aws-samples/asdi-smsl-wind-speed-data/ 35 | NotebookURL: https://github.com/aws-samples/asdi-smsl-wind-speed-data/blob/main/wind-speed-data.ipynb 36 | AuthorName: Frank Rubino 37 | Services: 38 | - Amazon SageMaker Studio Lab 39 | Tools & Applications: 40 | - Title: Radiant MLHub Dataset Registry 41 | URL: https://registry.mlhub.earth/ 42 | AuthorName: Radiant Earth 43 | AuthorURL: https://www.radiant.earth/ 44 | - Title: Challenge on Computer Vision for Crop Detection from Satellite Imagery 45 | URL: https://zindi.africa/competitions/iclr-workshop-challenge-2-radiant-earth-computer-vision-for-crop-recognition 46 | AuthorName: Radiant Earth 47 | AuthorURL: https://www.radiant.earth/ 48 | Publications: 49 | - Title: A Guide for Collecting and Sharing Ground Reference Data for Machine Learning Applications 50 | URL: https://medium.com/radiant-earth-insights/a-guide-for-collecting-and-sharing-ground-reference-data-for-machine-learning-applications-90664930925e 51 | AuthorName: Yonah Bromberg Gaber 52 | AuthorURL: https://www.linkedin.com/in/yonahbg/ 53 | - Title: Geo-Diverse Open Training Data as a Global Public Good 54 | URL: https://aws.amazon.com/blogs/publicsector/geo-diverse-open-training-data-as-a-global-public-good/ 55 | AuthorName: Hamed Alemohammad 56 | AuthorURL: https://www.linkedin.com/in/hamedalemohammad/ 57 | - Title: Creating a Machine Learning Commons for Global Development 58 | URL: https://medium.com/radiant-earth-insights/creating-a-machine-learning-commons-for-global-development-256ef3dd46aa 59 | AuthorName: Hamed Alemohammad 60 | AuthorURL: https://www.linkedin.com/in/hamedalemohammad/ 61 | -------------------------------------------------------------------------------- /datasets/rcm-ceos-ard.yaml: -------------------------------------------------------------------------------- 1 | Name: "RCM CEOS Analysis Ready Data | Données prêtes à l'analyse du CEOS pour le MCR" 2 | Description: "The [RADARSAT Constellation Mission (RCM)](https://www.asc-csa.gc.ca/eng/satellites/radarsat/) is Canada's third generation of Earth observation satellites. Launched on June 12, 2019, the three identical satellites work together to bring solutions to key challenges for Canadians. As part of ongoing [Open Government](https://open.canada.ca/en/about-open-government) efforts, NRCan produces a CEOS analysis ready data (ARD) of Canada landmass using a 30M Compact-Polarization standard coverage, every 12 days. RCM CEOS-ARD (POL) is the first ever polarimetric dataset [approved by the CEOS committee](https://ceos.org/ard/index.html#datasets). Previously, users were stuck ordering, downloading and processing RCM images (level 1) on their own, often with expensive software. This new dataset aims to remove these burdens with a new STAC catalog for discovery and direct download links. 3 | 4 |
5 |
6 | 7 | La mission de la Constellation RADARSAT (MCR) est la troisième génération de satellites d'observation de la Terre du Canada. Lancés le 12 juin 2019, les trois satellites identiques travaillent ensemble pour apporter des solutions aux principaux défis des Canadiens. Dans le cadre des efforts continus pour un gouvernement ouvert, RNCan produit des données prêtes à l'analyse CEOS (ARD) de la masse terrestre du Canada en utilisant une couverture standard de 30 m en polarisation compacte, tous les 12 jours. Les CEOS-ARD (POL) du MCR constituent le premier ensemble de données polarimétriques jamais [approuvé par le comité CEOS](https://ceos.org/ard/index.html#datasets). Auparavant, les utilisateurs étaient obligés de commander, de télécharger et de traiter eux-mêmes les images RCM (niveau 1), souvent à l'aide de logiciels coûteux. Ce nouvel ensemble de données vise à supprimer ces fardeaux avec un nouveau catalogue STAC à découvrir et à télécharger directement depuis S3." 8 | Documentation: https://www.asc-csa.gc.ca/eng/satellites/radarsat/ 9 | Contact: eodms-sgdot@nrcan-rncan.gc.ca 10 | ManagedBy: "[Natural Resources Canada](https://www.nrcan.gc.ca/)" 11 | UpdateFrequency: "The initial dataset will be Canada-wide, 30M Compact-Polarization standard coverage, every 12 days, per mission revisit frequency. 12 | 13 |
14 |
15 | 16 | L'ensemble de données initial couvrira l'ensemble du Canada, une couverture standard de 30 metres de polarisation compacte, tous les 12 jours, par fréquence de revisite de mission." 17 | Tags: 18 | - aws-pds 19 | - agriculture 20 | - earth observation 21 | - satellite imagery 22 | - geospatial 23 | - sustainability 24 | - disaster response 25 | - synthetic aperture radar 26 | - stac 27 | - ceos 28 | - analysis ready data 29 | License: "RCM image products are available free of charge, to the broadest extent possible, in order to promote the development of innovative products and services derived from SAR data. The Government of Canada retains ownership of all RCM data and image products. Intellectual property rights to value-added products (VAPs) created from the RCM image products will remain with the creator of the VAP. Acceptable uses of RCM image products are set out in the [RCM Public User License Agreement](https://www.asc-csa.gc.ca/eng/satellites/radarsat/access-to-data/public-user-license-agreement.asp), which is provided with each RCM image product. 30 | 31 |
32 |
33 | 34 | Les produits d'image de la MCR sont disponibles sans frais, le plus possible accessibles, pour encourager la mise au point de produits et services novateurs dérivés des données RSO. Le gouvernement du Canada conserve la propriété de toutes les données et de tous les produits d'image de la MCR. Les droits de propriété intellectuelle sur les produits à valeur ajoutée (PVA) créés à partir des produits d'image de la MCR demeureront la propriété du créateur du PVA. Les utilisations acceptables des produits d'image de la MCR sont énoncées dans le [contrat de licence d'utilisation](https://www.asc-csa.gc.ca/fra/satellites/radarsat/acces-aux-donnees/contrat-licence-utilisation.asp) fourni avec chaque produit d'image de la MCR" 35 | Resources: 36 | - Description: RCM CEOS Analysis Ready Data. Données prêtes à l'analyse (DPA) du CEOS pour le MCR 37 | ARN: arn:aws:s3:::rcm-ceos-ard 38 | Region: ca-central-1 39 | Type: S3 Bucket 40 | Explore: 41 | - '[EODMS STAC for RCM CEOS ARD](https://www.eodms-sgdot.nrcan-rncan.gc.ca/stac/collections/rcm-ard/items/)' 42 | DataAtWork: 43 | Publications: 44 | - Title: Synthetic Aperture Radar (CEOS-ARD SAR) 45 | URL: https://ceos.org/ard/files/PFS/SAR/v1.1/CEOS-ARD_PFS_Synthetic_Aperture_Radar_v1.1.pdf 46 | AuthorName: Committee on Earth Observation Satellites (CEOS) for developing the CEOS ARD Standards. Specific acknowledgement to François Charbonneau (NRCan) for contributions to the standard development through CEOS committee membership as well as application to Canadian RADARSAT data. 47 | AuthorURL: 48 | - Title: CEOS Analysis Ready Data 49 | URL: https://ceos.org/ard/ 50 | AuthorName: Committee on Earth Observation Satellites (CEOS) 51 | AuthorURL: https://ceos.org/ 52 | - Title: Sentinel-1 Global Backscatter Model (S1GBM) 53 | URL: https://researchdata.tuwien.ac.at/records/n2d1v-gqb91 54 | AuthorName: TU Wien Research Data 55 | AuthorURL: https://researchdata.tuwien.ac.at/ 56 | - Title: Copernicus Global Digital Elevation Model 57 | URL: https://dataspace.copernicus.eu/explore-data/data-collections/copernicus-contributing-missions/collections-description/COP-DEM 58 | AuthorName: European Space Agency (ESA) 59 | AuthorURL: https://www.esa.int/ -------------------------------------------------------------------------------- /datasets/satellogic-earthview.yaml: -------------------------------------------------------------------------------- 1 | Name: Satellogic EarthView dataset 2 | Description: Satellogic EarthView dataset includes high-resolution satellite images captured over all continents. The dataset is organized in Hive partition format and hosted by AWS. The dataset can be accessed via STAC browser or aws cli. Each item of the dataset corresponds to a specific region and date, with some of the regions revisited for additional data. The dataset provides Top-of-Atmosphere (TOA) reflectance values across four spectral bands (Red, Green, Blue, Near-Infrared) at a Ground Sample Distance (GSD) of 1 meter, accompanied by comprehensive metadata such as off-nadir angles, sun elevation, and other pertinent details. Users should note that due to an artifact in region delineation, a small number of regions present overlaps. 3 | Documentation: https://satellogic-earthview.s3.us-west-2.amazonaws.com/index.html 4 | Contact: https://www.satellogic.com/ 5 | ManagedBy: "[Satellogic](https://www.satellogic.com)" 6 | UpdateFrequency: New data will be made available periodically, with annual updates expected in the future covering the same or other new regions. 7 | Tags: 8 | - aws-pds 9 | - satellite imagery 10 | - earth observation 11 | - image processing 12 | - geospatial 13 | - computer vision 14 | - stac 15 | - cog 16 | License: "[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/deed.en)" 17 | Resources: 18 | - Description: Satellogic data includes TOA RGBN COG, VISUAL RGB COG files data and metadata 19 | ARN: arn:aws:s3:::satellogic-earthview 20 | Region: us-west-2 21 | Type: S3 Bucket 22 | RequesterPays: False 23 | Explore: 24 | - '[STAC Catalog](https://satellogic-earthview.s3.us-west-2.amazonaws.com/stac/catalog.json)' 25 | - '[STAC Browser](https://radiantearth.github.io/stac-browser/#/external/satellogic-earthview.s3.us-west-2.amazonaws.com/stac/catalog.json)' 26 | DataAtWork: 27 | Tutorials: 28 | - Title: Explore Satellogic EarthView in SageMaker Studio Lab (SMSL) 29 | URL: https://github.com/satellogic/satellogic-earthview/ 30 | NotebookURL: https://github.com/satellogic/satellogic-earthview/blob/main/satellogic_earthview_exploration.ipynb 31 | AuthorName: Javier Marin 32 | Services: 33 | - Amazon SageMaker Studio Lab 34 | Publications: 35 | - Title: "EarthView: A Large Scale Remote Sensing Dataset for Self-Supervision" 36 | URL: https://satellogic-earthview.s3.us-west-2.amazonaws.com/index.html 37 | AuthorName: Velázquez, Diego and Rodríguez, Pau and Alonso, Sergio and Gonfaus, Josep M. and González, Jordi and, Richarte, Gerardo and Marín, Javier and Bengio, Yoshua and Lacoste, Alexandre 38 | -------------------------------------------------------------------------------- /datasets/sentinel-1-rtc-indigo.yaml: -------------------------------------------------------------------------------- 1 | Name: Analysis Ready Sentinel-1 Backscatter Imagery 2 | Description: | 3 | The [Sentinel-1 mission](https://sentinel.esa.int/web/sentinel/missions/sentinel-1) is a constellation of 4 | C-band Synthetic Aperature Radar (SAR) satellites from the European Space Agency launched since 2014. 5 | These satellites collect observations of radar backscatter intensity day or night, regardless of the 6 | weather conditions, making them enormously valuable for environmental monitoring. 7 | These radar data have been processed from original Ground Range Detected (GRD) scenes into a Radiometrically 8 | Terrain Corrected, tiled product suitable for analysis. This product is available over the Contiguous United States (CONUS) 9 | since 2017 when Sentinel-1 data became globally available. 10 | Documentation: https://sentinel-s1-rtc-indigo-docs.s3-us-west-2.amazonaws.com/index.html 11 | Contact: For questions regarding data methodology or delivery, contact sentinel1@indigoag.com. 12 | ManagedBy: "[Indigo Ag, Inc.](https://www.indigoag.com/)" 13 | UpdateFrequency: Data updates are paused while we repair the processing pipeline, but the target is to update on a daily cadence for the dataset's spatial domain (CONUS). 14 | Collabs: 15 | ASDI: 16 | Tags: 17 | - satellite imagery 18 | Tags: 19 | - agriculture 20 | - aws-pds 21 | - disaster response 22 | - earth observation 23 | - environmental 24 | - geospatial 25 | - satellite imagery 26 | - cog 27 | - stac 28 | - synthetic aperture radar 29 | License: | 30 | The use of these data fall under the terms and conditions of the [Indigo Atlas Sentinel License](https://www.indigoag.com/forms/atlas-sentinel-license). 31 | Resources: 32 | - Description: Sentinel-1 RTC tiled data and metadata in a S3 bucket 33 | ARN: arn:aws:s3:::sentinel-s1-rtc-indigo 34 | Region: us-west-2 35 | Type: S3 Bucket 36 | Explore: 37 | - '[STAC V1.0.0 endpoint](https://scottyhq.github.io/sentinel1-rtc-stac/#/)' 38 | - Description: Simple Notification Service (SNS) topic for notification of new tile uploads 39 | ARN: arn:aws:sns:us-west-2:410373799403:sentinel-s1-rtc-indigo-object_created 40 | Region: us-west-2 41 | Type: SNS Topic 42 | DataAtWork: 43 | Tutorials: 44 | - Title: Compare Cloud-Optimized Geotiffs from Amazon Sustainability Data Initiative (ASDI) hosted on S3 using SageMaker Studio Lab (SMSL) 45 | URL: https://github.com/aws-samples/asdi-smsl-demo-delta 46 | NotebookURL: https://github.com/aws-samples/asdi-smsl-demo-delta/blob/main/Compare-GeoTiffs-S3.ipynb 47 | AuthorName: Gianfranco Rapino 48 | Services: 49 | - Amazon SageMaker Studio Lab 50 | -------------------------------------------------------------------------------- /datasets/sentinel-3.yaml: -------------------------------------------------------------------------------- 1 | Name: Sentinel-3 2 | Description: This data set consists of observations from the Sentinel-3 satellite of the European Commission’s Copernicus Earth Observation Programme. Sentinel-3 is a polar orbiting satellite that completes 14 orbits of the Earth a day. It carries the Ocean and Land Colour Instrument (OLCI) for medium resolution marine and terrestrial optical measurements, the Sea and Land Surface Temperature Radiometer (SLSTR), the SAR Radar Altimeter (SRAL), the MicroWave Radiometer (MWR) and the Precise Orbit Determination (POD) instruments. The satellite was launched in 2016 and entered routine operational phase in 2017. Data is available from July 2017 onwards. 3 | Documentation: https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel3Description.md 4 | Contact: sentinel3@meeo.it 5 | ManagedBy: "[Meteorological Environmental Earth Observation](http://www.meeo.it/)" 6 | UpdateFrequency: Daily 7 | Collabs: 8 | ASDI: 9 | Tags: 10 | - satellite imagery 11 | Tags: 12 | - aws-pds 13 | - oceans 14 | - earth observation 15 | - environmental 16 | - geospatial 17 | - land 18 | - satellite imagery 19 | - cog 20 | - stac 21 | License: https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice 22 | Resources: 23 | - Description: Sentinel-3 Near Real Time Data (NRT) format 24 | ARN: arn:aws:s3:::meeo-s3/NRT/ 25 | Region: eu-central-1 26 | Type: S3 Bucket 27 | - Description: Sentinel-3 Not Time Critical (NTC) format 28 | ARN: arn:aws:s3:::meeo-s3/NTC/ 29 | Region: eu-central-1 30 | Type: S3 Bucket 31 | - Description: Sentinel-3 Short Time Critical (STC) format 32 | ARN: arn:aws:s3:::meeo-s3/STC/ 33 | Region: eu-central-1 34 | Type: S3 Bucket 35 | - Description: Sentinel-3 Cloud Optimized GeoTIFF (COG) format 36 | ARN: arn:aws:s3:::meeo-s3-cog/ 37 | Region: eu-central-1 38 | Type: S3 Bucket 39 | Explore: 40 | - '[STAC V1.0.0 endpoint](https://meeo-s3.s3.amazonaws.com/)' 41 | DataAtWork: 42 | Tutorials: 43 | - Title: Accessing Sentinel-3 Data on S3 by MEEO 44 | URL: https://github.com/Sentinel-5P/data-on-s3/blob/master/notebooks/Sentinel3_Tutorial.ipynb 45 | AuthorName: Meteorological Environmental Earth Observation 46 | AuthorURL: http://www.meeo.it/ 47 | Tools & Applications: 48 | - Title: Sentinel-3 Toolbox 49 | URL: https://step.esa.int/main/toolboxes/sentinel-3-toolbox/ 50 | AuthorName: European Space Agency 51 | AuthorURL: https://www.esa.int/ 52 | - Title: Catalogue of data set 53 | URL: https://meeo-s3.s3.amazonaws.com/index.html#/?t=catalogs 54 | AuthorName: Meteorological Environmental Earth Observation 55 | AuthorURL: https://www.meeo.it/ 56 | Publications: 57 | - Title: Sentinel-3 Document Library 58 | URL: https://sentinel.esa.int/web/sentinel/user-guides 59 | AuthorName: European Space Agency 60 | AuthorURL: https://www.esa.int/ 61 | -------------------------------------------------------------------------------- /datasets/sentinel-products-ca-mirror.yaml: -------------------------------------------------------------------------------- 1 | Name: "Sentinel Near Real-time Canada Mirror | Miroir Sentinel temps quasi réel du Canada" 2 | Description: "The official Government of Canada (GC) 🍁 Near Real-time (NRT) Sentinel Mirror connected to the [EU Copernicus programme](https://www.copernicus.eu), focused on Canadian coverage. In 2015, [Canada joined the Sentinel collaborative ground segment](https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Canada_joins_Sentinel_collaborative_ground_segment) which introduced an NRT Sentinel mirror site for users and programs inside the Government of Canada (GC). In 2022, the [Commission signed a Copernicus Arrangement with the Canadian Space Agency](https://defence-industry-space.ec.europa.eu/signature-copernicus-arrangement-between-canadian-space-agency-and-european-commission-2022-05-16_en) with the aim to share each other’s satellite Earth Observation data on the basis of reciprocity. Further to this arrangement as well as ongoing [Open Government](https://open.canada.ca/en/about-open-government) efforts, the private mirror was made open to the public, here on the AWS Open Dataset Registry. 3 | 4 |
5 |
6 | 7 | Le Sentinel Mirror officiel du gouvernement du Canada (GC) 🍁 en temps quasi réel (NRT) connecté au [programme Copernicus de l'UE] (https://www.copernicus.eu), axé sur la couverture canadienne. En 2015, [le Canada a rejoint le segment terrestre collaboratif Sentinel](https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Canada_joins_Sentinel_collaborative_ground_segment) qui a introduit un site miroir NRT Sentinel pour les utilisateurs et les programmes au sein du gouvernement du Canada (GC). . En 2022, la [Commission a signé un accord Copernicus avec l'Agence spatiale canadienne](https://defence-industry-space.ec.europa.eu/signature-copernicus-arrangement-between-canadian-space-agency-and-european-commission-2022-05-16_fr) dans le but de partager mutuellement les données satellitaires d'observation de la Terre sur la base de la réciprocité. Suite à cet arrangement ainsi qu'aux efforts continus de [gouvernement ouvert](https://open.canada.ca/en/about-open-government), le miroir privé a été rendu ouvert au public, ici sur le registre des ensembles de données ouvertes AWS." 8 | Documentation: https://sentinel.esa.int/web/sentinel/home 9 | Contact: eodms-sgdot@nrcan-rncan.gc.ca 10 | ManagedBy: "[Natural Resources Canada](https://www.nrcan.gc.ca/)" 11 | UpdateFrequency: "Sentinel-1 is an NRT dataset retrieved from ESA within 90 minutes of satellite downlink. Non-NRT Sentinel-2 and Sentinel-3 are also updated as quickly as possible based on Canada coverage and availability at the source. 12 | 13 |
14 |
15 | 16 | Sentinel-1 est un ensemble de données NRT récupéré de l'ESA dans les 90 minutes suivant la liaison descendante du satellite. Sentinel-2 et Sentinel-3 non NRT sont également récupérés le plus rapidement possible en fonction de la couverture du Canada et de la disponibilité à la source." 17 | Tags: 18 | - aws-pds 19 | - agriculture 20 | - earth observation 21 | - satellite imagery 22 | - geospatial 23 | - sustainability 24 | - disaster response 25 | - synthetic aperture radar 26 | - stac 27 | License: "The access and use of Copernicus Sentinel data is available on a free, full and open basis through the Copernicus Data Space Ecosystem and shall be governed by the Legal Notice on the use of Copernicus Sentinel Data and Service published here: https://sentinels.copernicus.eu/documents/247904/690755/Sentinel_Data_Legal_Notice." 28 | Resources: 29 | - Description: Sentinel data over Canada | Données sentinelles au Canada. 30 | ARN: arn:aws:s3:::sentinel-products-ca-mirror 31 | Region: ca-central-1 32 | Type: S3 Bucket 33 | Explore: 34 | - '[EODMS STAC for Sentinel products](https://www.eodms-sgdot.nrcan-rncan.gc.ca/stac/)' 35 | 36 | -------------------------------------------------------------------------------- /datasets/sentinel5p.yaml: -------------------------------------------------------------------------------- 1 | Name: Sentinel-5P Level 2 2 | Description: This data set consists of observations from the Sentinel-5 Precursor (Sentinel-5P) satellite of the European Commission’s Copernicus Earth Observation Programme. Sentinel-5P is a polar orbiting satellite that completes 14 orbits of the Earth a day. It carries the TROPOspheric Monitoring Instrument (TROPOMI) which is a spectrometer that senses ultraviolet (UV), visible (VIS), near (NIR) and short wave infrared (SWIR) to monitor ozone, methane, formaldehyde, aerosol, carbon monoxide, nitrogen dioxide and sulphur dioxide in the atmosphere. The satellite was launched in October 2017 and entered routine operational phase in March 2019. Data is available from July 2018 onwards. 3 | Documentation: https://github.com/Sentinel-5P/data-on-s3/blob/master/DocsForAws/Sentinel5P_Description.md 4 | Contact: sentinel5p@meeo.it 5 | ManagedBy: "[Meteorological Environmental Earth Observation](http://www.meeo.it/)" 6 | UpdateFrequency: Daily 7 | Collabs: 8 | ASDI: 9 | Tags: 10 | - satellite imagery 11 | Tags: 12 | - aws-pds 13 | - air quality 14 | - atmosphere 15 | - earth observation 16 | - environmental 17 | - geospatial 18 | - satellite imagery 19 | - cog 20 | - stac 21 | License: https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice 22 | Resources: 23 | - Description: Sentinel-5p Near Real Time Data (NRTI). NetCDF format. 24 | ARN: arn:aws:s3:::meeo-s5p/NRTI/ 25 | Region: eu-central-1 26 | Type: S3 Bucket 27 | - Description: Sentinel-5p Off Line Data (OFFL) NetCDF format. 28 | ARN: arn:aws:s3:::meeo-s5p/OFFL/ 29 | Region: eu-central-1 30 | Type: S3 Bucket 31 | - Description: Sentinel-5p Reprocessed Data (RPRO) NetCDF format. 32 | ARN: arn:aws:s3:::meeo-s5p/RPRO/ 33 | Region: eu-central-1 34 | Type: S3 Bucket 35 | - Description: Sentinel-5p Cloud Optimised GeoTIFF (COGT). TIFF format. 36 | ARN: arn:aws:s3:::meeo-s5p/COGT/ 37 | Region: eu-central-1 38 | Type: S3 Bucket 39 | Explore: 40 | - '[STAC V1.0.0 endpoint](https://meeo-s5p.s3.amazonaws.com/index.html?t=catalogs)' 41 | DataAtWork: 42 | Tutorials: 43 | - Title: Accessing Sentinel-5P Data on S3 by MEEO 44 | URL: https://github.com/Sentinel-5P/data-on-s3/blob/master/notebooks/Sentinel5P_Tutorial.ipynb 45 | AuthorName: Meteorological Environmental Earth Observation 46 | AuthorURL: http://www.meeo.it/ 47 | Tools & Applications: 48 | - Title: The Atmospheric Toolbox 49 | URL: https://atmospherictoolbox.org/ 50 | AuthorName: European Space Agency 51 | AuthorURL: https://www.esa.int/ 52 | - Title: Catalogue of data set 53 | URL: https://meeo-s5p.s3.amazonaws.com/index.html#/?t=catalogs 54 | AuthorName: Meteorological Environmental Earth Observation 55 | AuthorURL: https://www.meeo.it/ 56 | Publications: 57 | - Title: Sentinel-5P TROPOMI Document Library 58 | URL: https://sentinel.esa.int/web/sentinel/user-guides/sentinel-5p-tropomi/document-library 59 | AuthorName: European Space Agency 60 | AuthorURL: https://www.esa.int/ 61 | 62 | -------------------------------------------------------------------------------- /datasets/umbra-open-data.yaml: -------------------------------------------------------------------------------- 1 | Name: Umbra Synthetic Aperture Radar (SAR) Open Data 2 | Description: Umbra satellites generate the highest resolution Synthetic Aperture Radar (SAR) imagery ever offered from space, up to 16-cm resolution. SAR can capture images at night, through cloud cover, smoke and rain. SAR is unique in its abilities to monitor changes. The Open Data Program (ODP) features over twenty diverse time-series locations that are updated frequently, allowing users to experiment with SAR's capabilities. We offer single-looked spotlight mode in either 16cm, 25cm, 35cm, 50cm, or 1m resolution, and multi-looked spotlight mode. The ODP also features an assorted collection of over 250+ images and counting of various locations around the world, ranging from emergency response, to gee-whiz sites. If you have a suggestion for a new location, feedback on the dataset, or any questions, contact us at umbra.space/open-data. 3 | Documentation: https://help.umbra.space/product-guide 4 | Contact: help@umbra.space 5 | ManagedBy: "[Umbra](http://umbra.space/)" 6 | UpdateFrequency: New data is added frequently. The frequent updates enable users to analyze the time-series data to detect changes in each location. Umbra's goal is to provide the user with a deep understanding on what's possible with our data. 7 | Tags: 8 | - aws-pds 9 | - synthetic aperture radar 10 | - stac 11 | - satellite imagery 12 | - earth observation 13 | - image processing 14 | - geospatial 15 | License: | 16 | All data is provided with a Creative Commons License ([CC by 4.0](https://umbra.space/open-license)), which gives you the right to do just about anything you want with it. 17 | Resources: 18 | - Description: Umbra Spotlight collects including GEC, SICD, SIDD, CPHD data and metadata 19 | ARN: arn:aws:s3:::umbra-open-data-catalog 20 | Region: us-west-2 21 | Type: S3 Bucket 22 | RequesterPays: False 23 | Explore: 24 | - '[Browse Bucket](http://umbra-open-data-catalog.s3-website.us-west-2.amazonaws.com/)' 25 | - '[STAC Browser](https://radiantearth.github.io/stac-browser/#/external/s3.us-west-2.amazonaws.com/umbra-open-data-catalog/stac/catalog.json)' 26 | -------------------------------------------------------------------------------- /datasets/usgs-lidar.yaml: -------------------------------------------------------------------------------- 1 | Name: USGS 3DEP LiDAR Point Clouds 2 | Description: The goal of the [USGS 3D Elevation Program ](https://www.usgs.gov/core-science-systems/ngp/3dep) (3DEP) is to collect elevation data in the form of light detection and ranging (LiDAR) data over the conterminous United States, Hawaii, and the U.S. territories, with data acquired over an 8-year period. This dataset provides two realizations of the 3DEP point cloud data. The first resource is a public access organization provided in [Entwine Point Tiles](https://entwine.io/entwine-point-tile.html) format, which a lossless, full-density, streamable octree based on [LASzip](https://laszip.org) (LAZ) encoding. The second resource is a [Requester Pays](https://docs.aws.amazon.com/AmazonS3/latest/dev/RequesterPaysBuckets.html) of the original, Raw LAZ (Compressed LAS) 1.4 3DEP format, and more complete in coverage, as sources with incomplete or missing CRS, will not have an ETP tile generated. Resource names in both buckets correspond to the USGS project names. 3 | Documentation: https://github.com/hobu/usgs-lidar/ 4 | Contact: https://github.com/hobu/usgs-lidar 5 | ManagedBy: "[Hobu, Inc.](https://hobu.co)" 6 | UpdateFrequency: Periodically 7 | Collabs: 8 | ASDI: 9 | Tags: 10 | - elevation 11 | Tags: 12 | - aws-pds 13 | - agriculture 14 | - elevation 15 | - disaster response 16 | - geospatial 17 | - lidar 18 | - stac 19 | License: US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensing-map-services-and-data-national-map 20 | Resources: 21 | - Description: Public access Entwine Point Tiles of most resources from the ``arn:aws:s3:::usgs-lidar`` bucket. 22 | ARN: arn:aws:s3:::usgs-lidar-public 23 | Region: us-west-2 24 | Type: S3 Bucket 25 | Explore: 26 | - '[STAC Catalog](https://usgs-lidar-stac.s3-us-west-2.amazonaws.com/ept/catalog.json)' 27 | - Description: A [Requester Pays](https://docs.aws.amazon.com/AmazonS3/latest/dev/RequesterPaysBuckets.html) Bucket of Raw LAZ 1.4 3DEP data. Data in this bucket is more complete in coverage than the EPT bucket, but it is not a complete 3DEP mirror. Some resources in this bucket also have incomplete and missing coordinate system information, which is why they might not be mirrored into the EPT bucket. 28 | ARN: arn:aws:s3:::usgs-lidar 29 | Region: us-west-2 30 | Type: S3 Bucket 31 | RequesterPays: True 32 | DataAtWork: 33 | Tutorials: 34 | - Title: Using Lambda Layers with USGS 3DEP LiDAR Point Clouds 35 | URL: https://github.com/hobu/usgs-lidar/tree/master/lambda 36 | AuthorName: Howard Butler 37 | AuthorURL: https://twitter.com/howardbutler 38 | Services: 39 | - AWS Lambda 40 | - Title: Extracting buildings and roads from AWS Open Data using Amazon SageMaker 41 | URL: https://aws.amazon.com/blogs/machine-learning/extracting-buildings-and-roads-from-aws-open-data-using-amazon-sagemaker/ 42 | AuthorName: Yunzhi Shi, Tianyu Zhang, and Xin Chen 43 | Services: 44 | - Amazon SageMaker 45 | - Title: WebGL Visualization of USGS 3DEP Lidar Point Clouds with Potree and Plasio.js 46 | URL: https://usgs.entwine.io/ 47 | AuthorName: Connor Manning 48 | AuthorURL: https://twitter.com/csmannin 49 | Tools & Applications: 50 | - Title: Jupyter Notebooks to enable programmatic access to cloud-hosted USGS 3D Elevation Program (3DEP) lidar data 51 | URL: https://github.com/OpenTopography/OT_3DEP_Workflows 52 | AuthorName: OpenTopography 53 | AuthorURL: https://opentopography.org/ 54 | - Title: OpenTopography access to 3DEP lidar point cloud data 55 | URL: https://portal.opentopography.org/datasets 56 | AuthorName: OpenTopography 57 | AuthorURL: https://opentopography.org/ 58 | - Title: Facebook Line of Sight Check 59 | URL: https://www.facebook.com/isptoolbox/line-of-sight-check/ 60 | AuthorName: Facebook 61 | AuthorURL: https://www.facebook.com/isptoolbox/ 62 | - Title: Equator - View, Process, and Download USGS 3DEP LiDAR data in-browser 63 | URL: https://equatorstudios.com/lidar-viewer/ 64 | AuthorName: Equator Studios 65 | AuthorURL: https://equatorstudios.com 66 | Publications: 67 | - Title: USGS 3DEP Lidar Point Cloud Now Available as Amazon Public Dataset 68 | URL: https://www.usgs.gov/news/usgs-3dep-lidar-point-cloud-now-available-amazon-public-dataset 69 | AuthorName: Department of the Interior, U.S. Geological Survey 70 | AuthorURL: https://www.usgs.gov 71 | - Title: Statewide USGS 3DEP Lidar Topographic Differencing Applied to Indiana, USA 72 | URL: https://www.mdpi.com/2072-4292/14/4/847/htm 73 | AuthorName: Chelsea Phipps Scott, Matthew Beckley, Minh Phan, Emily Zawacki, Christopher Crosby, Viswanath Nandigam, and Ramon Arrowsmith 74 | -------------------------------------------------------------------------------- /datasets/venus-l2a-cogs.yaml: -------------------------------------------------------------------------------- 1 | Name: VENUS L2A Cloud-Optimized GeoTIFFs 2 | Description: | 3 | The [Venµs science mission](https://www.theia-land.fr/en/product/venus/) is a joint research mission undertaken by CNES and ISA, 4 | the Israel Space Agency. It aims to demonstrate the effectiveness of high-resolution multi-temporal observation optimised through 5 | Copernicus, the global environmental and security monitoring programme. Venµs was launched from the Centre Spatial Guyanais by a 6 | VEGA rocket, during the night from 2017, August 1st to 2nd. Thanks to its multispectral camera (12 spectral bands in the visible 7 | and near-infrared ranges, with spectral characteristics provided [here](https://labo.obs-mip.fr/multitemp/?page_id=14229)), it 8 | acquires imagery every 1-2 days over 100+ areas at a spatial resolution of 4 to 5m. This dataset has been converted into Cloud 9 | Optimized GeoTIFFs (COGs). Additionally, SpatioTemporal Asset Catalog metadata are generated in a JSON file alongside the data. 10 | This dataset contains all of the Venus L2A datasets and will continue to grow as the Venus mission acquires new data over the 11 | preselected sites. 12 | Documentation: https://github.com/earthdaily/venus-on-aws/ 13 | Contact: Klaus Bachhuber - klaus.bachhuber@earthdaily.com 14 | ManagedBy: "[EarthDaily Analytics](https://earthdaily.com/)" 15 | UpdateFrequency: New Venus data are added regularly 16 | Tags: 17 | - aws-pds 18 | - agriculture 19 | - earth observation 20 | - satellite imagery 21 | - geospatial 22 | - image processing 23 | - natural resource 24 | - disaster response 25 | - cog 26 | - stac 27 | - activity detection 28 | - environmental 29 | - land cover 30 | License: https://creativecommons.org/licenses/by-nc/4.0/ 31 | Resources: 32 | - Description: Venus L2A dataset (COG) and metadata (STAC) 33 | ARN: arn:aws:s3:::venus-l2a-cogs 34 | Region: us-east-1 35 | Type: S3 Bucket 36 | RequesterPays: False 37 | Explore: 38 | - '[STAC Browser Venus L2A (COG) Catalog](https://radiantearth.github.io/stac-browser/#/external/venus-l2a-cogs.s3.us-east-1.amazonaws.com/catalog.json)' 39 | - Description: New Venus L2A dataset notifications, can subscribe with [Lambda](https://aws.amazon.com/lambda/) or [SQS](https://aws.amazon.com/sqs/). Message contains link to STAC record for each new dataset made available. 40 | ARN: arn:aws:sns:us-east-1:794383284256:venus-l2a-cogs-object_created 41 | Region: us-east-1 42 | Type: SNS Topic 43 | -------------------------------------------------------------------------------- /datasets/wb-light-every-night.yaml: -------------------------------------------------------------------------------- 1 | Name: World Bank - Light Every Night 2 | Description: Light Every Night - World Bank Nighttime Light Data – provides open access to all nightly imagery and data from the Visible Infrared Imaging Radiometer Suite Day-Night Band (VIIRS DNB) from 2012-2020 and the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) from 1992-2013. The underlying data are sourced from the NOAA National Centers for Environmental Information (NCEI) archive. Additional processing by the University of Michigan enables access in Cloud Optimized GeoTIFF format (COG) and search using the Spatial Temporal Asset Catalog (STAC) standard. The data is published and openly available under the terms of the World Bank’s open data license. 3 | Documentation: https://worldbank.github.io/OpenNightLights/wb-light-every-night-readme.html 4 | Contact: Trevor Monroe tmonroe@worldbank.org; Benjamin P. Stewart bstewart@worldbankgroup.org; Brian Min brianmin@umich.edu; Kim Baugh kim.baugh@noaa.gov 5 | ManagedBy: "[World Bank Group](https://www.worldbank.org/en/home)" 6 | UpdateFrequency: Quarterly 7 | Collabs: 8 | ASDI: 9 | Tags: 10 | - satellite imagery 11 | Tags: 12 | - disaster response 13 | - earth observation 14 | - satellite imagery 15 | - aws-pds 16 | - stac 17 | - cog 18 | License: "[World Bank Open Database License (ODbL)](https://creativecommons.org/licenses/by/4.0/)" 19 | Resources: 20 | - Description: Light Every Night dataset of all VIIRS DNB and DMSP-OLS nighttime satellite data 21 | ARN: arn:aws:s3:::globalnightlight 22 | Region: us-east-1 23 | Type: S3 Bucket 24 | Explore: 25 | - '[STAC 1.0.0-beta.2 endpoint](https://stacindex.org/catalogs/world-bank-light-every-night#/)' 26 | DataAtWork: 27 | Tutorials: 28 | - Title: Open Nighttime Lights 29 | URL: https://worldbank.github.io/OpenNightLights/welcome.html 30 | AuthorName: Daynan Crull, Trevor Monroe 31 | AuthorURL: https://worldbank.github.io/OpenNightLights/welcome.html 32 | Tools & Applications: 33 | - Title: Global Scale Nightlight Time Series Dataset 34 | URL: https://nightlight.eoatlas.org 35 | AuthorName: Alameen Najjar 36 | AuthorURL: https://github.com/eoameen 37 | - Title: High Resolution Electricity Access Indicators (HREA) - Settlement-level measures of electricity access, reliability, and usage. 38 | URL: http://www-personal.umich.edu/~brianmin/HREA/ 39 | AuthorName: Brian Min, Zachary O'Keeffe 40 | AuthorURL: http://www-personal.umich.edu/~brianmin/ 41 | - Title: Twenty Years of India Lights 42 | URL: http://nightlights.io 43 | AuthorName: Kwawu Mensan Gaba, Brian Min, Anand Thakker, Christopher Elvidge 44 | AuthorURL: http://nightlights.io 45 | Publications: 46 | - Title: Nighttime lights compositing using the VIIRS day-night band - Preliminary results. Proceedings of the Asia-Pacific Advanced Network 35 (2013)70-86. 47 | URL: https://journals.sfu.ca/apan/index.php/apan/article/view/8/0 48 | AuthorName: Kimberly Baugh, Feng-Chi Hsu, Christopher D. Elvidge, and Mikhail Zhizhin. 49 | - Title: Mainstreaming Disruptive Technologies in Energy. World Bank Report. 2019 50 | URL: http://documents.worldbank.org/curated/en/305771562750007469/Mainstreaming-Disruptive-Technologies-in-Energy 51 | AuthorName: Kwawu Mensan Gaba, Brian Min, Olaf Veerman, Kimberly Baugh 52 | - Title: Mapping city lights with nighttime data from the DMSP Operational Linescan System. Photogrammetric Engineering and Remote Sensing, 63(6)727-734. 53 | URL: https://www.asprs.org/wp-content/uploads/pers/97journal/june/1997_jun_727-734.pdf 54 | AuthorName: Elvidge, C.D., Baugh, K.E., Kihn, E.A., Kroehl, H.W. and Davis, E.R. 55 | - Title: Power and the Vote - Elections and Electricity in the Developing World. Cambridge. 2015. 56 | URL: https://www.cambridge.org/core/books/power-and-the-vote/8091820171410B4FA557B6EBC3A5DD06 57 | AuthorName: Brian Min 58 | - Title: "Detection of Rural Electrification in Africa using DMSP-OLS Night Lights Imagery. International Journal of Remote Sensing" 59 | URL: https://openknowledge.worldbank.org/handle/10986/16192 60 | AuthorName: Brian Min, Kwawu Mensan Gaba, Ousmane Fall Sarr, Alassane Agalassou. 61 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | leafmap 2 | pyyaml --------------------------------------------------------------------------------