├── .github └── workflows │ ├── action.yml │ └── markdown.links.config.json ├── .gitignore ├── LICENSE ├── README.md ├── contributing.md ├── create-bookmarks-from-readme ├── 2020-04-17-awesome-eo-code-bookmarks.html ├── 2020-06-21-awesome-eo-code-bookmarks.html ├── 2021-11-23-awesome-eo-code-bookmarks.html ├── create_bookmarks_html.py └── readme.md └── presentations ├── 20200501_Awesome-EarthObservation-Code_Barsc_Cuddle.pdf ├── 20200617_FOSS4GUK_Awesome-EarthObservation-Code.pdf └── readme.md /.github/workflows/action.yml: -------------------------------------------------------------------------------- 1 | name: Check Markdown links 2 | 3 | on: 4 | push: 5 | branches: 6 | - master 7 | schedule: 8 | # Run everyday at 9:00 AM (See https://pubs.opengroup.org/onlinepubs/9699919799/utilities/crontab.html#tag_20_25_07) 9 | - cron: "0 9 * * *" 10 | 11 | jobs: 12 | markdown-link-check: 13 | runs-on: ubuntu-latest 14 | steps: 15 | - uses: actions/checkout@master 16 | - uses: gaurav-nelson/github-action-markdown-link-check@v1 17 | with: 18 | use-quiet-mode: 'no' 19 | use-verbose-mode: 'yes' 20 | config-file: '.github/workflows/markdown.links.config.json' 21 | max-depth: 0 22 | file-path: './README.md' 23 | -------------------------------------------------------------------------------- /.github/workflows/markdown.links.config.json: -------------------------------------------------------------------------------- 1 | { 2 | "ignorePatterns": [ 3 | { 4 | "pattern": "^http://example.net" 5 | } 6 | ], 7 | "replacementPatterns": [ 8 | { 9 | "pattern": "^.attachments", 10 | "replacement": "file://some/conventional/folder/.attachments" 11 | }, 12 | { 13 | "pattern": "^/", 14 | "replacement": "{{BASEURL}}/" 15 | } 16 | ], 17 | "httpHeaders": [ 18 | { 19 | "urls": ["https://example.com"], 20 | "headers": { 21 | "Authorization": "Basic Zm9vOmJhcg==", 22 | "Foo": "Bar" 23 | } 24 | } 25 | ], 26 | "timeout": "20s", 27 | "retryOn429": true, 28 | "retryCount": 5, 29 | "fallbackRetryDelay": "30s", 30 | "aliveStatusCodes": [429, 200] 31 | } 32 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider 2 | # Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 3 | 4 | # User-specific stuff 5 | .idea/**/workspace.xml 6 | .idea/**/tasks.xml 7 | .idea/**/usage.statistics.xml 8 | .idea/**/dictionaries 9 | .idea/**/shelf 10 | 11 | # AWS User-specific 12 | .idea/**/aws.xml 13 | 14 | # Generated files 15 | .idea/**/contentModel.xml 16 | 17 | # Sensitive or high-churn files 18 | .idea/**/dataSources/ 19 | .idea/**/dataSources.ids 20 | .idea/**/dataSources.local.xml 21 | .idea/**/sqlDataSources.xml 22 | .idea/**/dynamic.xml 23 | .idea/**/uiDesigner.xml 24 | .idea/**/dbnavigator.xml 25 | 26 | # Gradle 27 | .idea/**/gradle.xml 28 | .idea/**/libraries 29 | 30 | # Gradle and Maven with auto-import 31 | # When using Gradle or Maven with auto-import, you should exclude module files, 32 | # since they will be recreated, and may cause churn. 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6 | 7 |

8 | 9 | The [#scenefromabove podcast](https://scenefromabove.podbean.com/) aimed to be a mix of news, opinion, discussion and interviews. I am no longer involved in the podcast, however it is still going
10 | 11 | ## Latest news 12 | 13 | I have written a blog post about how this repo came into being. It includes a video of a talk I gave about it AND a podcast episode devoted to it. http://www.acgeospatial.co.uk/awesome-earthobservation-code/ 14 | 15 | Please note that this is not offically an awesome list. 16 | 17 | Update March 2024 Added a load of STAC links and some opendatacube ones. I accept PR's and you get a mention in the contributors file. 18 | 19 | A note of caution During the QC of links I note that the vast majority are 18 + months old or considerbly older. Some repos are retired and still visible, some code is > 10 years old. Tread carefully. 20 | 21 | Annotations are based on the headers - and where available - on the github accounts 22 | 23 |
24 | 25 | 26 | 27 |
28 | 29 | 30 | # Contents 31 | 32 | | [Earth Observation introduction](#earth-observation-introduction) |
33 | 34 | | [Open EO](#open-eo) | [remotesensing.info](#remote-sensinginfo) | [Python processing](#python-processing-of-optical-imagery-non-deep-learning) | [Resources for R](#resources-for-r) | [Languages other than Python and R](#languages-other-than-python-and-r) | [Training and Learning](#training-and-learning) | [Deep Learning & Machine Learning](#deep-learning-and-machine-learning) | [GDAL of course](#gdal-of-course) | [Earth Observation coding on YouTube](#earth-observation-coding-on-youtube) | [Google Earth Engine](#earth-engine) | [Open Data Cube](#open-data-cube) | [Planetary Computer](#planetary-computer) | [QGIS and Grass](#qgis-and-grass) | [Climate & Weather resources](#climate-and-weather-based-resources) | [DEM projects](#dem-projects) | [SAR](#sar) | [LiDAR](#lidar) | [GEDI](#gedi) | [InSAR](#insar) | [Landuse](#landuse) | [Visualisation](#visualisation) | [EO code Competitions](#eo-code-competitions) | [ARD links](#ard-links) | [Useful EO code based twitter accounts](#useful-eo-code-based-twitter-accounts) | [List of Great GitHub accounts](#great-github-accounts) | [EO Geospatial companies or orgs making big contributions](#eo-geospatial-companies-or-orgs-making-big-contributions) | 35 | 36 | | [Cloud Native Geospatial](#cloud-native-geospatial) | [STAC](#stac) | [COG](#cog) 37 | 38 | These sections are non EO code specific, but included to be relevant
39 | | [Interesting Non EO parts Python](#interesting-non-eo-parts-python) | [Interesting Non EO parts other languages](#interesting-non-eo-parts-other-languages) | [Data](#data) | [A footnote on awesome](#a-footnote-on-awesome) 40 | 41 | #### Start Here 42 | 43 | ## Earth Observation Introduction 44 | 45 | If you are not familiar with Earth Observation then these links may help set context before you start using data. I didn't initially aim at including links like these but if you are not familiar with Earth Observation then some good resources to get you going may help prior to diving into code. 46 | 47 | - [Earth Observation Text books](https://www.eoa.org.au/earth-observation-textbooks) - Earth Observation: Data, Processing and Applications is an Australian Earth Observation (EO) community undertaking to describe EO data, processing and applications in an Australian context and includes a wide range of local case studies to demonstrate Australia’s increasing usage of EO data. 48 | - [ESA newcomers guide](https://business.esa.int/newcomers-earth-observation-guide) - The aim of this guide is to help non-experts in providing a starting point in the decision process for selecting an appropriate Earth Observation (EO) solution. 49 | - [The state of satellites](https://landscape.satsummit.io/) - The satellite systems we use to capture, analyze, and distribute data about the Earth are improving every day, creating bold new opportunities for impact in global development. 50 | - [Landsats Enduring Legacy](https://my.asprs.org/landsat) - pdf download over 600 pages of remote sensing! 51 | 52 | You may also wish to navigate a search of the terms `satellite-imagery` and `earth-observation` to get the latest list of topics that have these terms in their headers 53 | 54 | - [satellite-imagery](https://github.com/topics/satellite-imagery) 55 | - [earth-observation](https://github.com/topics/earth-observation) 56 | 57 | Two excellent videos (approx 20mins) about `Earth observation` 58 | 59 | [I Couldn't Find a Video Explaining Satellite Images, So I Made One](https://www.youtube.com/watch?v=xy5qR0cBFGs) 60 | 61 | [How Radar Satellites See through Clouds (Synthetic Aperture Radar Explained)](https://www.youtube.com/watch?v=zMsCyEAOrh0) 62 | 63 | Not sure the best place for data catalogs is but this is a good start if that interests you [Data Catalogs](https://github.com/opengeos/geospatial-data-catalogs) 64 | 65 | ## Open EO 66 | 67 | OpenEO covers many of the bases, hard to know whether to break it into different categories, it has many components. At present I mention it here at the start only.
68 | 69 | - [Open EO](https://openeo.org/) - openEO develops an open API to connect `R`, `Python`, `JavaScript` and other clients to big Earth observation cloud back-ends in a simple and unified way. 70 | - [openeo-processes](https://github.com/Open-EO/openeo-processes) - Interoperable processes for openEO's big Earth observation cloud processing [website](https://processes.openeo.org/) 71 | 72 | ## Remote Sensing.info 73 | 74 | All links have been changed - update your pointers Oct 2022 75 | 76 | remotesening.info warrents its own section, the vast array of tools and processing software is incredible 77 | [RemoteSensing](https://github.com/remotesensinginfo) - Short tutorials and reference to useful software tools for the acquisition and processing of remote sensed Earth Observation data 78 | - [RSGISLib](http://rsgislib.org/rsgislib.html) - The Remote Sensing and GIS software library (RSGISLib) is a collection of tools for processing remote sensing and GIS datasets. The tools are accessed using `Python` bindings. 79 | - [ARCSI](https://github.com/remotesensinginfo/arcsi) - Software to automate the production of optical analysis ready data (ARD) from Landsat, Sentinel-2 and others 80 | - [eodatadown](https://github.com/remotesensinginfo/eodatadown) - The Earth Observation Data Downloader (EODataDown) is a tool for automatically downloading and processing EO data to an analysis ready data product. This software forms a core component of a monitoring system based on EO data. 81 | - more to come.. 82 | 83 | ## `Python` processing of optical imagery (non deep learning) 84 | 85 | This section full of great code and projects related to processing optical satellite imagery with `Python` . This section is under review Sept 2020 and being split into further categories - please suggest groupings or re assignments if needed - the idea is to make the Python code examples here easier to find. Categories are highly subjective. 86 | 87 | ### Download 88 | 89 | - [EODAG](https://eodag.readthedocs.io/en/latest/) - Command line tool and a plugin-oriented Python framework for searching, aggregating results and downloading remote sensed images while offering a unified API for data access regardless of the data provider. 90 | - [Sedas API](https://github.com/SatelliteApplicationsCatapult/sedas_pyapi) - `Python` client library for the SeDAS API 91 | - [esa_sentinel](https://github.com/jonas-eberle/esa_sentinel) - ESA Sentinel Search & Download API 92 | - [get_modis](https://github.com/jgomezdans/get_modis) - Downloading MODIS data from the USGS repository `Python` 93 | - [landsatexplore](https://github.com/yannforget/landsatxplore) - Search and download Landsat scenes from EarthExplorer. `Python` 94 | - [pylandsat](https://github.com/yannforget/pylandsat) - Search, download, and preprocess Landsat imagery `Python` 95 | - [Sentinel-download](https://github.com/olivierhagolle/Sentinel-download) - Automated download of Sentinel-2 L1C data from ESA (through wget) `Python` 96 | - [sentinelsat](https://github.com/sentinelsat/sentinelsat) - Search and download Copernicus Sentinel satellite images [sentinelsat docs](https://sentinelsat.readthedocs.io/en/stable/) `Python` 97 | - [LANDSAT-Download](https://github.com/olivierhagolle/LANDSAT-Download) - Automated download of LANDSAT data from USGS website 98 | - [Landsat-Util](https://github.com/developmentseed/landsat-util) - A utility to search, download and process Landsat 8 satellite imagery `Python` 99 | - [data-prep-scripts](https://lpdaac.usgs.gov/tools/data-prep-scripts/) - This collection of `R` and `Python` scripts can be used to download data and perform basic data processing functions such as georeferencing, reprojecting, converting, and reformatting data. All scripts are available for download from the LP DAAC User Resources [BitBucket Code Repository](https://git.earthdata.nasa.gov/projects/LPDUR). 100 | - [Stream NASA data directly into Python objects](https://nbviewer.jupyter.org/gist/scottyhq/a1ddbb12f97764860160370229b19261) - Skip the download! Stream NASA data directly into Python objects from [blog post](https://medium.com/pangeo/intake-stac-nasa-4cd78d6246b7) 101 | - [sat-extractor](https://github.com/FrontierDevelopmentLab/sat-extractor) - Extract Satellite Imagery from public constellations at scale `Python` 102 | 103 | ### Processing imagery - post processing 104 | 105 | - [StarFM for Python](https://github.com/nmileva/starfm4py) - The STARFM fusion model for `Python` (image fusion) 106 | - [Remote Sensing indicies calc](https://github.com/rander38/Remote-Sensing-Indices-Derivation-Tool) - Calculate spectral remote sensing indices from satellite imagery 107 | - [EarthPy](https://github.com/earthlab/earthpy) - A package built to support working with spatial data using open source python. [docs](https://earthpy.readthedocs.io/en/latest/) 108 | - [RasterFrames / pyrasterframes](https://github.com/locationtech/rasterframes) - brings together Earth-observation (EO) data access, cloud computing, and DataFrame-based data science. [docs](https://rasterframes.io/) 109 | - [SIF tools](https://github.com/cfranken/SIF_tools) - some tools for accessing OCO-2 data 110 | - [SIAC](https://github.com/MarcYin/SIAC) - A sensor invariant Atmospheric Correction (SIAC) [alg doc](http://www2.geog.ucl.ac.uk/~ucfafyi/Atmo_Cor/) 111 | - [S2_TOA_TO_LAI](https://github.com/MarcYin/S2_TOA_TO_LAI) - From Sentinel 2 TOA reflectance to LAI 112 | - [cresi](https://github.com/avanetten/cresi) - Road network extraction from satellite imagery, with speed and travel time estmates 113 | - [6S_emulator](https://github.com/samsammurphy/6S_emulator) - Atmospheric correction in Python using a 6S emulator 114 | - [bv](https://github.com/daleroberts/bv) - Quickly view satellite imagery, hyperspectral imagery, and machine learning image outputs directly in your iTerm2 terminal. `Python` 115 | - [mapchete](https://github.com/ungarj/mapchete) - Tile-based geodata processing using rasterio & Fiona `Python` 116 | - [unmixing](https://github.com/arthur-e/unmixing) - Interactive tools for spectral mixture analysis of multispectral raster data in `Python` 117 | - [landsat and sentinel fusion](https://github.com/yannforget/landsat-sentinel-fusion) - Complementarity Between Sentinel-1 and Landsat 8 Imagery for Built-Up Mapping in Sub-Saharan Africa `Python` 118 | - [Planet Movement](https://github.com/rhammell/planet-movement) - Find and process Planet image pairs to highlight object movement. `Python` 119 | 120 | - [cedar-datacube](https://github.com/ceholden/cedar-datacube) - cedar - Create Earth engine Datacubes of Analytical Readiness `Python` [docs](https://ceholden.github.io/cedar-datacube/master/) 121 | - [stems - Spatio-temporal Tools for Earth Monitoring Science](https://github.com/ceholden/stems) - Spatio-temporal Tools for Earth Monitoring Science `Python` [docs](https://ceholden.github.io/stems/master/) 122 | - [ipyearth](https://github.com/davidbrochart/ipyearth) - An IPython Widget for Earth Maps `Python` 123 | - [Python-for-remote-sensing](https://github.com/Seyed-Ali-Ahmadi/Python-for-Remote-Sensing) - `Python` codes for remote sensing applications will be uploaded. [blog](https://earthobserv.com/) 124 | - [esda dissertation](https://github.com/Rabscuttler/esda-dissertation) - MSc Energy Systems & Data Analytics dissertation project notebooks - identifying solar PV from aerial imagery with computer vision `Python` 125 | - [geff_notebooks](https://github.com/cvitolo/geff_notebooks) - Jupyter notebooks to post-process fire danger data using `Python`/`xarray` 126 | 127 | - [river-width](https://github.com/redfoxgis/river-width) - Extracts water features from 4 band NAIP imagery and calculates river metrics. `Python` 128 | - [get_river_width](https://github.com/briannapagan/get_river_width/blob/master/get_river_width.py) - Find the river width (and other properties) from a masked water image `Python` 129 | - [extract_water](https://github.com/redfoxgis/extract_water/blob/master/extract_water.py) - Extract water from nIR imagery `Python` 130 | - [pyresample](https://github.com/pytroll/pyresample) - Geospatial image resampling in `Python` 131 | - [spatialist](https://github.com/johntruckenbrodt/spatialist) - A `Python` module for spatial data handling 132 | - [CometTS](https://github.com/CosmiQ/CometTS) - Comet Time Series Toolset for working with a time-series of remote sensing imagery and user defined polygons 133 | - [Telluric](https://github.com/satellogic/telluric) - telluric is a `Python` library to manage vector and raster geospatial data in an interactive and easy way 134 | - [onearth](https://github.com/nasa-gibs/onearth) - High-performance web services for tiled raster imagery and vector tiles `Python` 135 | - [geocube](https://github.com/corteva/geocube) - Tool to convert geopandas vector data into rasterized xarray data. `Python` [docs](https://corteva.github.io/geocube/stable/) 136 | - [Opensource-OBIA_processing_chain](https://github.com/tgrippa/Opensource_OBIA_processing_chain) - An open-source semi-automated processing chain for urban OBIA classification. `Grass` `Python` 137 | - [verde](https://github.com/fatiando/verde) - Processing and gridding spatial data using Green's functions 138 | - [s2p](https://github.com/cmla/s2p) - Satellite Stereo Pipeline `Python` 139 | - [xcube](https://github.com/dcs4cop/xcube) - xcube is a `Python` package for generating and exploiting data cubes powered by xarray, dask, and zarr 140 | - [geonotebook](https://github.com/OpenGeoscience/geonotebook) - A Jupyter notebook extension for geospatial visualization and analysis `Python` 141 | - [tatortot](https://github.com/GeoBigData/tatortot) - Prototype for a simple image annotation tool `Python` 142 | - [tiletanic](https://github.com/DigitalGlobe/tiletanic) - `Python` library to support generalized geographic tiling schemes 143 | - [Intro to Python GIS](https://automating-gis-processes.github.io) - Great free 3-day course by the University of Helsinki on GIS processing with Python 144 | - [openaq-s5](https://github.com/JamesOConnor/openaq-s5) - Map openaq data onto Sentinel5P data using AWS lambda 145 | - [vegetation health](https://github.com/tommylees112/vegetation_health) - Predicting vegetation health from precipitation and temperature 146 | - [Satellite-Image-Analysis](https://github.com/MasterPhysicist/Satellite-Image-Analysis) - PlanetScope, Landsat-8 and Sentinel-2 Image analysis `Python` codes 147 | - [felicette](https://github.com/plant99/felicette) - Satellite imagery for dummies. `Python` 148 | - [CostalSat](https://github.com/kvos/CoastSat) - Global shoreline mapping tool from satellite imagery `Python` 149 | 150 | - [Python-Remote-Sensing-Scripts](https://github.com/JavierLopatin/Python-Remote-Sensing-Scripts) - `Python` 3. X scripts for remote sensing processing 151 | - [fc-up42](https://github.com/petescarth/fc-up42) - UP42 Block for Fractional Cover calculation from Sentinel 2 L2A Data `Python` 152 | - [Opensource_OBIA_processing_chain](https://github.com/tgrippa/Opensource_OBIA_processing_chain) - An open-source semi-automated processing chain for urban OBIA classification. 153 | - [nansat](https://github.com/nansencenter/nansat) - Scientist friendly Python toolbox for processing 2D satellite Earth observation data. `Python`[docs](https://nansat.readthedocs.io/en/latest/index.html) 154 | - [nansat-lite](https://gitlab.com/jobel-open-source/nansat-lite) - nansat-lite is not a full nansat build for `Python` 3.5. Only bits of code from main classes, to start with. Eventually, if need it, more code will be added. 155 | - [IEO](https://github.com/DrGuy/ieo) - Irish Earth Observation (IEO) remote sensing data processing Python module `Python` 156 | - [IEOtools](https://github.com/DrGuy/IEOtools) - Tools for managing Earth observation data. Currently only supports Landsat imagery `Python` 157 | - [pykic](https://github.com/EkicierNico/pykic) - 'Python' module for remote sensing and GIS domain (image/signal, vector, miscellaneous processing) 158 | - [ukis-csmask](https://github.com/dlr-eoc/ukis-csmask) - masks clouds and cloud shadows in Sentinel-2, Landsat-8, Landsat-7 and Landsat-5 images `Python` 159 | - [jeolib-pyjeo](https://github.com/ec-jrc/jeolib-pyjeo) - pyjeo is a library for image processing for geospatial data implemented in JRC Ispra. `Python` 160 | - [pyrgis](https://github.com/PratyushTripathy/pyrsgis) - This repository cointains the source code of the 'pyrsgis' `Python` package. 161 | - [EOReader](https://github.com/sertit/eoreader) - Opensource `Python` library reading optical and SAR sensors, loading and stacking bands in a sensor-agnostic way. 162 | - [LandSurfaceClustering](https://github.com/lhalloran/LandSurfaceClustering) - Land surface classification using remote sensing data with unsupervised machine learning (k-means) `Python` 163 | 164 | ### Cloud Native Geospatial 165 | - [aws-sat-api-py](https://github.com/RemotePixel/remotepixel-api) - Process Satellite data using AWS Lambda functions 166 | - [GeoLambda](https://github.com/developmentseed/geolambda) - Create and deploy Geospatial AWS Lambda functions `Python` 167 | - [rio-viz](https://github.com/developmentseed/rio-viz) - Visualize Cloud Optimized GeoTIFF in browser `html` `Python` 168 | - [Sentinel-s3](https://github.com/developmentseed/sentinel-s3) - `Python` libraries for extracting Sentinel-2's metadata from Amazon S3 169 | - [geocore](https://github.com/Canadian-Geospatial-Platform/geocore) - GeoCore is an Open Source Cloud Native (AWS) Geospatial Catalog | GeoCore est un catalogue géospatial Open Source Cloud Native (AWS) 170 | - [cng-workshop](https://github.com/Element84/cng-workshop) - Intro to cloud-native geospatial workshop 171 | - [cloud-native-geospatial](https://github.com/ua-datalab/cloud-native-geospatial) - resource [introduction to cloud native geospatial](https://ua-datalab.github.io/cloud-native-geospatial/) 172 | 173 | #### STAC 174 | - [stac-utils](https://github.com/stac-utils) - Tools for working with SpatioTemporal Asset Catalogs (STAC) (perhaps worth going here first for STAC) `Python` `Javascript` 175 | - [pystac](https://github.com/stac-utils/pystac) - `Python` library for working with any SpatioTemporal Asset Catalog (STAC) 176 | - [stactools](https://github.com/stac-utils/stactools) - Command line utility and `Python` library for STAC 177 | - [pystac-client](https://github.com/stac-utils/pystac-client) - `Python` client for STAC Catalogs and APIs 178 | - [pgstac](https://github.com/stac-utils/pgstac) - Schema, functions and a `Python` library for storing and accessing STAC collections and items in `PostgreSQL` 179 | - [stac-fastapi](https://github.com/stac-utils/stac-fastapi) - STAC API implementation with FastAPI. `Python` 180 | - [stac-fastapi-pgstac](https://github.com/stac-utils/stac-fastapi-pgstac) - PostgreSQL backend for stac-fastapi using pgstac 181 | - [STAC Spec](https://github.com/radiantearth/stac-spec) - SpatioTemporal Asset Catalog specification - making geospatial assets openly searchable and crawlable 182 | - [stac-validator](https://github.com/stac-utils/stac-validator) - Validator for the stac-spec `Python` 183 | - [stackstac](https://github.com/gjoseph92/stackstac) - Turn a list of STAC items into a 4D xarray DataArray `Python` 184 | - [stac-nb](https://github.com/darrenwiens/stac-nb) - STAC in Jupyter Notebooks `Python` 185 | - [qgis-stac-plugin](https://github.com/stac-utils/qgis-stac-plugin) - QGIS plugin for reading STAC APIs `Python` 186 | - [easystac](https://github.com/cloudsen12/easystac) - A `Python` package for simple STAC queries 187 | - [stac-utils](https://github.com/stac-utils/stac-task) - Provides a class interface for running custom algorithms on STAC ItemCollections `Python` 188 | - [pgstac](https://github.com/stac-utils/pgstac) - Schema, functions and a python library for storing and accessing STAC collections and items in PostgreSQL 189 | - [pystac-client](https://github.com/stac-utils/pystac-client) - `Python` client for searching STAC APIs 190 | - [stac-asset](https://github.com/stac-utils/stac-asset) - Read and download STAC Assets, using a variety of authentication schemes 191 | - [stac-server](https://github.com/stac-utils/stac-server) - A Node-based STAC API, AWS Serverless, OpenSearch `Javascript` 192 | - [elastic search](https://github.com/stac-utils/stac-fastapi-elasticsearch-opensearch) - Elasticsearch backend for stac-fastapi with Opensearch support. `Python` 193 | - [stac4s](https://github.com/stac-utils/stac4s) - A `Scala` library with primitives to build applications using the SpatioTemporal Asset Catalogs specification 194 | - [stac-rs](https://github.com/stac-utils/stac-rs) - `Rust` implementation of the SpatioTemporal Asset Catalog (STAC) specification 195 | - [stac-table](https://github.com/stac-utils/stac-table) 196 | - [stac-fields](https://github.com/stac-utils/stac-fields) - A minimal STAC library that contains a list of STAC fields with some metadata and helper functions for styling as HTML. `Javascript` 197 | - [titiler-pgstac](https://github.com/stac-utils/titiler-pgstac) - TiTiler + PgSTAC 198 | - [stac-api-validator](https://github.com/stac-utils/stac-api-validator) - A STAC API validation client `Python` 199 | - [xpystac](https://github.com/stac-utils/xpystac) - For extending xarray.open_dataset to accept pystac objects `Python` 200 | - [stac-pydantic](https://github.com/stac-utils/stac-pydantic) - Pydantic data models for the STAC spec `Python` 201 | - [stac-migrate](https://github.com/stac-utils/stac-migrate) - A tool to migrate Items, Catalogs and Collections from old versions to the most recent one. `Javascript` 202 | - [stac-node-validator](https://github.com/stac-utils/stac-node-validator) - Simple validator for STAC Items, Catalogs, and Collections. STAC 1.0.0 compliant! `Javascript` 203 | - [stac-geoparquet](https://github.com/stac-utils/stac-geoparquet) - Convert STAC items to geoparquet. `Python` 204 | - [stac-index](https://github.com/stac-utils/stac-index) - A service that lists all available and registered STAC catalogs and APIs. 205 | - [stac-check](https://github.com/stac-utils/stac-check) - Linting and validation tool for STAC assets 206 | - [stac-terminal](https://github.com/stac-utils/stac-terminal) - Output info on STAC Items in the terminal 207 | - [stac-layer](https://github.com/stac-utils/stac-layer) - Visualize a STAC Item or Collection on a Leaflet Map 208 | - [pgstac-rs](https://github.com/stac-utils/pgstac-rs) - `Rust` interface to pgstac 209 | - [stac-rs](https://github.com/stac-utils/stac-rs) - Tools and libraries for the SpatioTemporal Asset Catalog (STAC) specification, written in `Rust` 210 | 211 | #### COG 212 | - [COG Validator](https://github.com/rouault/cog_validator) - Cloud Optimized GeoTIFF validation service 213 | - [titiler](https://github.com/developmentseed/titiler) - A modern dynamic tile server built on top of `FastAPI` and `Rasterio/GDAL`. 214 | - [cogeo-mosaic](https://github.com/developmentseed/cogeo-mosaic) - Create and use COG mosaic based on mosaicJSON `Python` 215 | - [Sentinel-2-cog](https://github.com/developmentseed/sentinel-2-cog) - Convert Sentinel-2 JPEG 2000 to COG with AWS Lambda `Python` 216 | - [COG Dumper](https://github.com/mapbox/COGDumper) - Dumps tiles out of a cloud optimized geotiff `Python` 217 | - [async-cog-reader](https://github.com/geospatial-jeff/async-cog-reader) - Read Cloud Optimized GeoTiffs without GDAL`Python` 218 | - [aiocogeo](https://github.com/geospatial-jeff/aiocogeo) - Asynchronous cogeotiff reader `Python` 219 | - [cogeotiff](https://github.com/blacha/cogeotiff) - High performance cloud optimised geotiff reader 220 | - [ecw-converter](https://github.com/lifebit-ai/ecw-converter) - Dockerised `Python` scripts & Nextflow pipeline for converting ecw files to either geotiffs or Cloud Optimised Geotiffs (COGs) 221 | - [COG pptx/pdf](https://github.com/saheelBreezo/Cloud-Optimised-Geotiff/blob/master/Talk/Cloud_Optimized_GeoTIFF_Blue_Sky_Analytics.pdf) - talk on COG 222 | 223 | ### Case studies / Projects 224 | 225 | - [Povetry predition using satellite imagery](https://github.com/carsonluuu/Poverty-Prediction-by-Satellite-Imagery) - Poverty Prediction by Combination of Satellite Imagery 226 | - [Python from space](https://github.com/kscottz/PythonFromSpace) - `Python` Examples for Remote Sensing 227 | - [count blue pixels](https://github.com/craic/count_shelters) - This project is an experiment in using simple image processing techniques on satellite images downloaded from Google Maps in order to quantify the relative density of temporary shelters in adjacent qudarants. `Python` `Ruby` 228 | - [Satellite imagery analysis with Python](https://github.com/parulnith/Satellite-Imagery-Analysis-with-Python) - Getting acquainted with the concept of satellite imagery data and how it can be analyzed to investigate real-world environmental and humanitarian challenges. `Python` `Jupyter Notebooks` [associated blog](https://medium.com/analytics-vidhya/satellite-imagery-analysis-with-python-3f8ccf8a7c32) 229 | - [Satellite imagery in Pakistan](https://github.com/iam-mhaseeb/Satellite-Imagery-Analysis-of-Vegetation-in-Southern-Pakistan) - This repository contains a study how we can examine the vegetation cover of a region with the help of satellite data. The notebook in this repository aims to familiarise with the concept of satellite imagery data and how it can be analyzed to investigate real-world environmental and humanitarian challenges. 230 | - [SentinelBot](https://github.com/JamesOConnor/Sentinel_bot) - A twitter bot which processes raw sentinel data `Python` [SentinelBot on twitter](https://twitter.com/sentinel_bot) 231 | - [ap-latem](https://github.com/dymaxionlabs/ap-latam) - Detection of slums and informal settlements from satellite imagery `Python` 232 | - [local_structire_wpb-severity](https://github.com/mikoontz/local-structure-wpb-severity) - Analysis of drone imagery to characterize forest structure and severity of a tree killing insect `Python` 233 | - [Truck_Detection_Sentinel2_COVID19](https://github.com/hfisser/Truck_Detection_Sentinel2_COVID19) - This repository is designated to detecting trucks using Sentinel-2 data. `Python` 234 | - [Artificial Intelligence for Geospatial Analysis with Pytorch’s TorchGeo (multi parts)](https://towardsdatascience.com/artificial-intelligence-for-geospatial-analysis-with-pytorchs-torchgeo-part-1-52d17e409f09) - An end-to-end deep learning geospatial segmentation project using Pytorch and TorchGeo packages - [code](https://gist.github.com/cordmaur/d050973aa3ed980023e9239183a2cb66#file-earthsurfacewater_medium_2-ipynb) 235 | 236 | ### Company specific examples 237 | 238 | (you may need to create an account to use these resources) 239 | 240 | - [Planet notebooks](https://github.com/planetlabs/notebooks) - interactive notebooks from Planet Engineering `Python` 241 | - [Planet-client-API](https://github.com/planetlabs/planet-client-python) - `Python` client for Planet APIs 242 | - [Maxar GDBx tools](https://github.com/DigitalGlobe/gbdxtools) - Python SDK for using GBDX. 243 | - [gdbx-surface-water](https://github.com/gena/gbdx-surface-water) - Reservoir surface area detection with Digital Globe imagery and Bayesian methods 244 | - [SentinelHub-py](https://github.com/sentinel-hub/sentinelhub-py) - Download and process satellite imagery in Python using Sentinel Hub services. 245 | - [sentinel2-cloud-detector](https://github.com/sentinel-hub/sentinel2-cloud-detector) - Sentinel Hub Cloud Detector for Sentinel-2 images in `Python` 246 | - [Orbit predictor](https://github.com/satellogic/orbit-predictor) - Python library to propagate satellite orbits. 247 | - [up42-py](https://github.com/up42/up42-py) - Python SDK for UP42, the geospatial marketplace and developer platform. `Python` 248 | - [S2-superresolution](https://github.com/up42/s2-superresolution) - Deep Learning-based algorithm to upsample all Sentinel-2 bands to 10m. Also an example how to use GPUs on UP42. `Python` 249 | - [icecube](https://github.com/iceye-ltd/icecube) - Create time-series datacubes for supervised machine learning with ICEYE SAR images. `Python` 250 | 251 | ### Reflectance / pre processing 252 | 253 | - [Landsat7 errors](https://github.com/gena/landsat7-errors) - Identifies errors in raw values of Landsat 7 254 | - [PyProSail](https://github.com/robintw/PyProSAIL) - Python interface to the ProSAIL leaf/canopy reflectance model 255 | - [Py6S](https://github.com/robintw/Py6S) - A `Python`interface to the 6S Radiative Transfer Model 256 | - [prosail](https://github.com/jgomezdans/prosail) - `Python` bindings for the PROSAIL canopy reflectance model 257 | - [ACOLITE_MR](https://github.com/acolite/acolite_mr) - ACOLITE_MR: Atmospheric correction for aquatic applications of metre-scale satellites 258 | - [radiometric_normalization](https://github.com/planetlabs/radiometric_normalization) - Implementation of radiometric normalization workflows `Python` 259 | - [color_balance](https://github.com/planetlabs/color_balance) - Balance your colors! `Python` 260 | - [data-retrieval-in-EO](https://gitlab.com/raul.lezameta/data-retrieval-in-EO/-/tree/master) - data-retrieval-in-EO, a project with reports from TU wien 261 | 262 | ### Python libraries related to EO 263 | 264 | - [rasterio](https://github.com/mapbox/rasterio) - Rasterio reads and writes geospatial raster datasets 265 | - [Xarray pyconuk 2018](https://github.com/robintw/XArray_PyConUK2018) - Code and slides for my talk at PyCon UK 2018 on XArray `Python` 266 | - [RasterStats](https://github.com/perrygeo/python-rasterstats) - Summary statistics of geospatial raster datasets based on vector geometries. `Python` 267 | - [SatPy](https://github.com/pytroll/satpy) - `Python` package for earth-observing satellite data processing 268 | - [pyimpute](https://github.com/perrygeo/pyimpute) - Spatial classification and regression using Scikit-learn and Rasterio `Python` 269 | - [dask-rasterio](https://github.com/dymaxionlabs/dask-rasterio) - Read and write rasters in parallel using Rasterio and Dask `Python` 270 | - [rioxarray](https://github.com/corteva/rioxarray) - geospatial xarray extension powered by rasterio [docs](https://corteva.github.io/rioxarray/stable/) 271 | - [xarray-spatial](https://github.com/makepath/xarray-spatial) - Raster-based Spatial Analysis for `Python` 272 | - [actinia core](https://github.com/mundialis/actinia_core) - Actinia Core is an open source REST API for scalable, distributed, high performance processing of geographical data that uses mainly GRASS GIS for computational tasks. `Python` 273 | - [actinia satellite plugin](https://github.com/mundialis/actinia_satellite_plugin) - This actinia plugin is designed for efficient satellite data handling, especially Landsat and Sentinel-2 scenes `Python` 274 | - [Whitebox Python](https://github.com/giswqs/whitebox-python) - WhiteboxTools `Python` Frontend 275 | - [ukis-pysat](https://github.com/dlr-eoc/ukis-pysat) - generic classes and functions to query, access and process multi-spectral and SAR satellite images 276 | 277 | ### Testing your code 278 | 279 | - [image-similarity-measures](https://pypi.org/project/image-similarity-measures/) - Implementation of eight evaluation metrics to access the similarity between two images. `Python` 280 | - [fake-geo-images](https://pypi.org/project/fake-geo-images/) - A module to programmatically create geotiff images which can be used for unit tests. `Python` 281 | 282 | ## Resources for `R` 283 | 284 | R is not my area of expertise so this section is lighter than I'd like, plus I'd love to know what is a useful resource 285 | Books! [Geospatial R Books](https://www.bigbookofr.com/geospatial.html) - some `R` books on geospatial 286 | 287 | - [R-Spatial](https://rspatial.org/raster/rs/1-introduction.html) - This book provides a short introduction to satellite data analysis with R. 288 | - [Remote Sensing analysis with R](https://rspatial.org/raster/rs/index.html) - Builds on above R-Spatial 289 | - [GDAL Cubes](https://cran.r-project.org/web/packages/gdalcubes/index.html) - Earth Observation Data Cubes from Satellite Image Collections. Also [here on github](https://github.com/appelmar/gdalcubes_R) 290 | - [R code for ML in Sat imagery](https://gist.github.com/franzalex/a95e227cab9b146a6092) - # Random Forest image classification Adapted from [stackoverflow](http://gis.stackexchange.com/a/57786/12899). 291 | - [whiteboxR](https://github.com/giswqs/whiteboxR) - An R frontend of the advanced geospatial data analysis platform - [whitebox-tools](https://github.com/jblindsay/whitebox-tools). 292 | - [RasterVIS](https://cran.r-project.org/web/packages/rasterVis/index.html) - Methods for enhanced visualization and interaction with raster data. It implements visualization methods for quantitative data and categorical data, both for univariate and multivariate rasters. It also provides methods to display spatiotemporal rasters, and vector fields. 293 | - [Landsat](https://cran.r-project.org/web/packages/landsat/index.html) - Processing of Landsat or other multispectral satellite imagery. Includes relative normalization, image-based radiometric correction, and topographic correction options. 294 | - [rnoaa](https://github.com/ropensci/rnoaa) - R interface to many NOAA data APIs 295 | - [MODISTools](https://github.com/ropensci/MODISTools) - Interface to the MODIS Land Products Subsets Web Services [Docs](https://docs.ropensci.org/MODISTools/) 296 | - [A Step-by-Step Guide to Making 3D Maps with Satellite Imagery in R](https://www.tylermw.com/a-step-by-step-guide-to-making-3d-maps-with-satellite-imagery-in-r/) - Walk you through [on] how to obtain the data required to make these types of maps, as well as the R code used to generate them 297 | - [landsatlinkr](https://github.com/jdbcode/LandsatLinkr) - An automated system for creating spectrally consistent and cloud-free Landsat image time series stacks from a combination of MSS, TM, ETM+, and OLI sensors [project](http://jdbcode.github.io/LandsatLinkr/) 298 | - [planetR](https://github.com/bevingtona/planetR) - (early development) R tools to search, activate and download satellite imagery from the Planet API 299 | - [ForestTools](https://github.com/andrew-plowright/ForestTools) - Detect and segment individual tree from remotely sensed data 300 | - [lidR](https://github.com/Jean-Romain/lidR) - `R` package for airborne LiDAR data manipulation and visualisation for forestry application. Plus [lidRplugins](https://github.com/Jean-Romain/lidRplugins) - Extra functions and algorithms for lidR package 301 | - [Spatiotemporal Arrays: Raster and Vector Datacubes](https://github.com/r-spatial/stars) - Spatiotemporal Arrays, Raster and Vector Data Cube 302 | - [getSpatialData](https://github.com/16EAGLE/getSpatialData) - An `R` package making it easy to query, preview, download and preprocess multiple kinds of spatial data [docs](https://jakob.schwalb-willmann.de/getSpatialData/) 303 | - [RStoolbox](https://bleutner.github.io/RStoolbox/) - RStoolbox is a R package providing a wide range of tools for your every-day remote sensing processing needs. 304 | - [rHarmonics](https://github.com/MBalthasar/rHarmonics) - `R` package for harmonic modelling of time-series data 305 | - [rerddap](https://github.com/ropensci/rerddap) - `R` client for working with ERDDAP servers [docs](https://docs.ropensci.org/rerddap/) reference the [ERDDAP Server](https://upwell.pfeg.noaa.gov/erddap/index.html) 306 | - [Spatial_Data_in_R](https://github.com/joheisig/Spatial_Data_in_R) - SWIRL-course on spatial data in `R` 307 | - [cognition-datasources](https://github.com/geospatial-jeff/cognition-datasources) - Standardized query interface for searching geospatial assets via STAC. 308 | - [caliver](https://github.com/ecmwf/caliver) - caliver: CALIbration and VERification of gridded fire danger models `R` 309 | - [clip_time_series](https://github.com/lecrabe/clip_time_series) - create snippets of Landsat and Sentinel imagery 310 | - [RGISTools](https://github.com/spatialstatisticsupna/RGISTools) - Tools for Downloading, Customizing, and Processing Time Series of Satellite Images from Landsat, MODIS, and Sentinel 311 | - [Grassland-Species-Classification](https://github.com/JavierLopatin/Grassland-Species-Classification) - Codes for: Javier Lopatin, Fabian E. Fassnacht, Teja Kattenborn, Sebastian Schmidtlein. Mapping plant species in mixed grassland communities using close range imaging spectroscopy. Remote Sensing of Environment 201, 12-23. `R` 312 | - [UAV-InvasiveSpp](https://github.com/JavierLopatin/UAV-InvasiveSpp) - Mapping invasive tree species in Chile using UAV `R` 313 | - [Peatland-carbon-stock](https://github.com/JavierLopatin/Peatland-carbon-stock) - Codes for: Lopatin, J., et al. (2019). Using aboveground vegetation attributes as proxies for mapping peatland belowground carbon stocks. Remote Sens. Environ. 231, 111217 `R` 314 | - [SpeciesRichness-GLMvsRF-LiDAR](https://github.com/JavierLopatin/SpeciesRichness-GLMvsRF-LiDAR) - `R`-codes for: Lopatin, J., Dolos, K., Hernández, J., Galleguillos, M., Fassnacht, F. E. (2016): Comparing Generalized Linear Models and random forest to model vascular plant species richness using LiDAR data in a natural forest in central Chile. Remote Sensing of Environment 173, pp. 200–210. 10.1016/j.rse.2015.11.029 315 | - [tree_segmentation](https://github.com/redfoxgis/tree_segmentation) - LiDAR tree segmentation `R` 316 | - [swdt](https://github.com/be-marc/swdt) - Sentinel-1 Water Dynamics Toolkit `R` 317 | 318 | - [What_are_data_cubes](https://edzer.github.io/UseR2020/#What_are_data_cubes) - Analyzing and visualising spatial and spatiotemporal data cubes - Part I 319 | - [classifying_satellite_imagery_in_R](https://urbanspatial.github.io/classifying_satellite_imagery_in_R/) - For this tutorial, we use Landsat 8 imagery from Calgary 320 | - [planetR](https://github.com/bevingtona/planetR) - `R` tools to search, activate and download satellite imagery from the Planet API. 321 | - [Landsat_land_surface_temperature](https://github.com/alyssakullberg/Landsat_land_surface_temperature) - `R` Estimate land surface temperature using Landsat satellite imagery. 322 | - [Living England Project](https://github.com/naturalengland/Living_England) - Sharing workflows created by the Living England project, Natural England. Predominantly in `R` 323 | 324 | ## Languages other than `Python` and `R` 325 | 326 | - [Georust](https://github.com/georust) - A collection of geospatial tools and libraries written in `Rust` 327 | - [ArchGDAL - Julia](https://github.com/yeesian/ArchGDAL.jl) - `Julia` A high level API for GDAL - Geospatial Data Abstract 328 | - [ArchGDAL docs](http://yeesian.com/ArchGDAL.jl/latest/) 329 | - [Julia_Geospatial](https://github.com/acgeospatial/Julia_Geospatial) - Examples for a blog series on Geospatial `Julia` using ArchGDAL 330 | - [GeoTrellis homepage](https://geotrellis.io/) - GeoTrellis is a geographic data processing engine for high performance applications. `Scala` 331 | - [GeoTrellis on Github - Scala](https://github.com/locationtech/geotrellis) 332 | - [GDAL with GoLang](https://github.com/lukeroth/gdal) - `Go` (golang) wrapper for GDAL, the Geospatial Data Abstraction Library 333 | - [C++ gdalcubes](https://github.com/appelmar/gdalcubes) - Earth observation data cubes from GDAL image collections `C++` 334 | - [RSGLib](https://github.com/remotesensinginfo/rsgislib) - The remote sensing and GIS software library (RSGISLib) is a set of `C++` libraries and commands for the processing of spatial data (raster and vector). Functionality is via `Python` interface though 335 | - [Perl extension for GDAL](https://metacpan.org/pod/Geo::GDAL) - Geo:: GDAL - `Perl` extension for the GDAL library for geospatial data 336 | - [PDAL](https://github.com/PDAL/PDAL) - PDAL is Point Data Abstraction Library. GDAL for point cloud data. 337 | - [force](https://github.com/davidfrantz/force) - Framework for Operational Radiometric Correction for Environmental monitoring in `c` 338 | - [LLR-landTrendr](https://github.com/jdbcode/LLR-LandTrendr) - Landsat-based Detection of Trends in Disturbance and Recovery algorimth modified to accept LandsatLinkr-processed imagery. `IDL` 339 | - [Global Forest Watch](https://github.com/Vizzuality/gfw) - Global Forest Watch: An online, global, near-real time forest monitoring tool 340 | - [conda recipes](https://github.com/yannforget/conda-recipes) - Conda recipes for remote sensing `Shell` 341 | - [Landsat-solar-elevation](https://github.com/jdbcode/landsat-solar-elevation) - A web app that plots annual solar elevation at the time of Landsat overpass for locations throughout the earth `JavaScript` 342 | - [staccato](https://github.com/planetlabs/staccato) - `Java` implementation of the STAC spec 343 | - [stac4s](https://github.com/azavea/stac4s) -a `scala` library with primitives to build applications using the SpatioTemporal Asset Catalogs specification 344 | - [stac-browser](https://github.com/radiantearth/stac-browser) - A Vue-based STAC browser intended for static + dynamic deployment 345 | - [EO Browser Custom Scripts](https://github.com/sentinel-hub/custom-scripts) - A repository of custom scripts to be used with Sentinel Hub `JavaScript` 346 | - [sentinelhub-js](https://github.com/sentinel-hub/sentinelhub-js) - Download and process satellite imagery in `JavaScript` or `TypeScript` using Sentinel Hub services. 347 | - [s3tbx](https://github.com/senbox-org/s3tbx) - A toolbox for the OLCI and SLSTR instruments on board of ESA's Sentinel-3 satellite - `Java` 348 | - [s2tbx](https://github.com/senbox-org/s2tbx) - Sentinel 2 Toolbox (s2tbx) - `Java` 349 | - [s1tbx](https://github.com/senbox-org/s1tbx) - The Sentinel-1 Toolbox - `Java` 350 | - [snap_engine](https://github.com/senbox-org/snap-engine) - ESA Earth Observation Toolbox and `Java` Development Platform 351 | - [Worldview](https://github.com/nasa-gibs/worldview) - Interactive interface for browsing global, full-resolution satellite imagery `Javascript` application [here](https://worldview.earthdata.nasa.gov/) 352 | - [Orfeo ToolBox](https://gitlab.orfeo-toolbox.org/orfeotoolbox/otb) (OTB)- An open-source project for state-of-the-art remote sensing, including a fast image viewer, apps callable from `Bash`, `Python` or QGIS, and a powerful `C++` API. 353 | - [landsat_preprocess](https://github.com/ceholden/landsat_preprocess) - IPython notebook documenting a workflow for preprocessing Landsat data `Shell` 354 | - [stac-mode-validator](https://github.com/m-mohr/stac-node-validator) - Simple proof-of-concept to validate STAC Items, Catalogs, Collections and core extensions with node. `JavaScript` 355 | - [aiforearth-landcover-app](https://github.com/vannizhang/aiforearth-landcover-app) - web mapping app to test, tweak and train the land cover classification from a deep neural network model 356 | - [tiffhax](https://github.com/emilyselwood/tiffhax) - tiff metadata hex viewer `Go` 357 | - [Fmask](https://github.com/GERSL/Fmask) - The software called Fmask (Function of mask) is used for automated clouds, cloud shadows, and snow masking for Landsats 4-8 and Sentinel 2 images. `Matlab` 358 | - [resto](https://github.com/jjrom/resto) - A metadata catalog and search engine for geospatialized data `PHP` Stac! 359 | - [pktools](http://pktools.nongnu.org/html/index.html) - pktools is a suite of utilities written in `C++` for image processing with a focus on remote sensing applications. It relies on the Geospatial Data Abstraction Library ([GDAL](http://www.gdal.org)) and OGR. 360 | - [iris](https://github.com/ESA-PhiLab/iris) - Semi-automatic tool for manual segmentation of multi-spectral and geo-spatial imagery. `Javascript` 361 | 362 | ## Training and learning 363 | 364 | - [Earth Data Lab](https://github.com/earthlab/earthlab.github.io) - A site dedicated to tutorials, course and other learning materials and resources developed by the Earth Lab team 365 | - [EO College Github](https://github.com/EO-College) 366 | - [tomography_tutorial](https://github.com/EO-College/tomography_tutorial) - A tutorial for Synthetic Aperture Radar Tomography 367 | - [profLewis-geog0111](https://github.com/profLewis/geog0111) - UCL Geography: 4th year course, Scientific Computing 368 | - [Intro to Geospatial Vector and Raster](https://carpentries-incubator.github.io/geospatial-python/) - Data Carpentry’s aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. 369 | - [Andrew Cutts Github](https://github.com/acgeospatial) - I am an Earth Observation and Geospatial enthusiast, primarily using `Python` to automate and process images at scale using computer vision 370 | - [Satellite Imagery Python](https://github.com/acgeospatial/Satellite_Imagery_Python) - Sample sample scripts and notebooks on processing satellite imagery 371 | - [Geospatial Python Programming Course](https://github.com/acgeospatial/Geospatial_Python_CourseV1) - This is an collection of blog posts turned into a course format 372 | - [Open Geo Tutorial V2](https://github.com/patrickcgray/open-geo-tutorial) - Tutorial of fundamental remote sensing and GIS methodologies using open source software in `Python` 373 | - [Open Geo Tutorial V1](https://github.com/ceholden/open-geo-tutorial) - Tutorial of basic remote sensing and GIS methodologies using open source software (GDAL in `Python` or `R`) 374 | - [Foss4gUKJupyter](https://github.com/samfranklin/foss4guk19-jupyter) - FOSS4G UK 2019 Workshop "Geoprocessing with Jupyter Notebooks" 375 | - [Geoprocessing with Python - GIS circa 2009](https://www.gis.usu.edu/~chrisg/python/2009/) - This material is really old and some of it is outdated (not all, though!). One of these days I might get around to putting newer class materials online, but you're stuck with this for now. 376 | - [training-workshop](https://github.com/planetlabs/training-workshop) - This repo contains all materials used on Planet's training workshop for Bahrain Defense Force 377 | - [sentinel](https://github.com/techforspace/sentinel) - Repository created for the Earth Observation Sentinel project (use with SNAP) `Python` 378 | - [Python for Geospatial Analysis](https://www.tomasbeuzen.com/python-for-geospatial-analysis/README.html) - A crashcourse introduction to using Python to wrangle, plot, and model geospatial data `Python` 379 | - [pyGIS](https://github.com/mmann1123/pyGIS) - pyGIS is an online textbook covering all the core geospatial functionality available in `Python`. This includes handling vector and raster data, satellite remote sensing, machine learning and deep learning applications 380 | 381 | ## Deep learning and Machine Learning 382 | - [future learn course - artificial intelligence for earth monitoring](https://www.futurelearn.com/courses/artificial-intelligence-for-earth-monitoring) 383 | - [Segment-geospatial](https://github.com/opengeos/segment-geospatial) - A `Python` package for segmenting geospatial data with the Segment Anything Model (SAM). [docs](https://samgeo.gishub.org/) 384 | 385 | #### Curated lists 386 | 387 | [Robin Cole on satellite imagery and deep learning resources](https://github.com/robmarkcole/satellite-image-deep-learning) - Resources for deep learning with satellite & aerial imagery. This is the best place to go for this topic I've removed 95% of the associated links from awesome-eo-code as it is just a repetition. 388 | 389 | - [awesome-satellite-imagery-datasets](https://github.com/chrieke/awesome-satellite-imagery-datasets) - List of satellite image training datasets with annotations for computer vision and deep learning. `ARCHIVED REPO` 390 | - [Deep Vector](https://github.com/deepVector/geospatial-machine-learning) - A curated list of resources focused on Machine Learning in Geospatial Data Science. 391 | 392 | #### Labelling 393 | - [satellite-imagery-labeling-tool](https://github.com/microsoft/satellite-imagery-labeling-tool) - This is a lightweight web-interface for creating and sharing vector annotations over satellite/aerial imagery scenes. 394 | 395 | 396 | ## GDAL of course 397 | 398 | - [GDAL Cheat Sheet](https://github.com/dwtkns/gdal-cheat-sheet) - Cheat sheet for GDAL/OGR command-line tools 399 | - [GDAL / OGR cookbook](https://pcjericks.github.io/py-gdalogr-cookbook/) - This cookbook has simple code snippets on how to use the Python GDAL/OGR API 400 | - [GDAL tutorial](https://jakobmiksch.eu/post/gdal_ogr/) - This blogpost gives in an introduction to GDAL/OGR and explains how the various command line tools can be used. 401 | - [docker-base-gdal](https://github.com/perrygeo/docker-gdal-base) - A base docker image for geospatial applications 402 | - [An Introduction to GDAL](https://www.youtube.com/watch?v=N_dmiQI1s24) - An Introduction to GDAL - Robert Simmon 403 | - [A Gentle Introduction to GDAL prt 1](https://medium.com/planet-stories/a-gentle-introduction-to-gdal-part-1-a3253eb96082) - command line working 404 | - [A Gentel Introduction to GDAL prt 2](https://medium.com/planet-stories/a-gentle-introduction-to-gdal-part-2-map-projections-gdalwarp-e05173bd710a) - Map Projections 405 | - [A Gentel Introduction to GDAL prt 3](https://medium.com/planet-stories/a-gentle-introduction-to-gdal-part-3-geodesy-local-map-projections-794c6ff675ca) - Geodesy 406 | - [loam](https://github.com/azavea/loam) - `Javascript` wrapper for GDAL in the browser 407 | - [mrf](https://github.com/nasa-gibs/mrf) - GDAL-compatible file format driver designed for fast access to imagery 408 | 409 | ## Earth Observation coding on YouTube 410 | 411 | (presenters listed where possible)
412 | There are many videos relating to Earth Observation and coding, especially Python. This is really such a small collection of videos here. I have attempted to only include ones with good audio and code examples. 413 | 414 | - [xArray at PyConUK2018 - Robin Wilson](https://www.youtube.com/watch?v=Dgr_d8iEWk4) - Processing thousands of satellite images to understand air quality in the UK - it's efficient and easy with XArray 415 | - [Visualizing & Analyzing Earth Science Data Using PyViz & PyData - Julia Signell](https://youtu.be/-XMXNmGRk5c?t=455) - In this talk, we'll work through some specific workflows and explore how various tools - such as Intake, Dask, Xarray, and Datashader - can be used to effectively analyze and visualize these data. Working from within the notebook, we'll iteratively build a product that is interactive, scalable, and deployable. 416 | - [Hands on Satellite Imagery 2019 edition - Sara Safavi](https://www.youtube.com/watch?v=j15MryznWn4) - In this tutorial, gain hands-on experience exploring Planet’s publicly-available satellite imagery and using Python tools for geospatial and time-series analysis of medium- and high-resolution imagery data. Using free & open source libraries, learn how to perform foundational imagery analysis techniques and apply these techniques to real satellite data. 417 | - [Python from space - Katherine Scott](https://www.youtube.com/watch?v=rUUgLsspTZA&t) - In this talk we will work through a jupyter notebook that covers the satellite data ecosystem and the python tools that can be used to sift through and analyze that data. Topics include python tools for using Open Street Maps data, the Geospatial Data Abstraction Library (GDAL), and OpenCV and NumPy for image processing. 418 | - [Remote Sening with Python in Jupyter](https://www.youtube.com/watch?v=OsgZSlv4t-U) - In this video we're looking at using Google Earth Engine in Jupyter with the Python API. 419 | - [Writing Image Processing Algorithms with ArcGIS/ArcPy - Jamie Drisdelle](https://www.youtube.com/watch?v=FenT61l-xyQ) - learn how your algorithms can integrate with the raster processing and visualization pipelines in ArcGIS. We’ll demonstrate the concept and discuss the API by diving deep into a few interesting examples with a special focus on multidimensional scientific rasters. 420 | - [Google Earth Engine Python - Qiusheng Wu](https://www.youtube.com/playlist?list=PLAxJ4-o7ZoPccOFv1dCwvGI6TYnirRTg3) - Introducing the geemap Python package for interactive mapping with Google Earth Engine and ipyleaflet. 421 | - [Google Earth Engine EE101 Condensed - Noel Gorelick](https://www.youtube.com/watch?v=m1ejxSi3l8s) - Introduction to the Earth Engine API and a conceptual overview of key functionality such as compositing, reducing, mapping, zonal statistics and cluminating with building a small app. 422 | - [Image classification with RandomForests using the R language](https://www.youtube.com/watch?v=fal4Jj81uMA)In this video I show how to import a Landsat image into R and how to extract pixel data to train and fit a RandomForests model. I also explain how to conduct image classification and how to speed it up through parallel processing. 423 | - [GeoPython 2019 stream](https://www.youtube.com/watch?v=3KRYObqpMlk) - 17:23 Machine Learning for Land Use/Landcover Statistics of Switzerland (Adrian Meyer), 50:58 How to structure geodata, 1:18:13 Terrain segmentation with label bootstrapping for lidar datasets, case of doline detection (Rok Mihevc), 2:34:41 Bias in machine learning, 3:06:23 Software for planning research aircraft missions (Reimar Bauer), 3:32:38 How Technology Moves Fast (PJ Hagerty) , 5:02:05 Spotting Sharks with the TensorFlow Object Detection API (Andrew Carter), 5:40:23 Center for Open Source Data and AI Technologies (CODAIT), 6:03:40 Bayesian modeling with spatial data using PyMC3 (Shreya Khurana) (Sound at 6:04:23 ^^), 7:02:45 Understanding and Implementing Generative Adversarial Networks(GANs) (Anmol Krishan Sachdeva), 7:37:00 Messaging with Satellites from Anywhere on the Planet (Andrew Carter), 8:04:52 Automation of the definition and optimizatino of census sampling areas using AREA (GRID3) (Freja Hunt), 8:35:26 Coastline Mapping with Python, Satellite Imagery and Computer Vision (Rachel Keay) 424 | - [Google Earth Engine in QGIS](https://www.youtube.com/playlist?list=PL8jLygUmAosykCyE-5Pr6zpcB_UqnbFiZ) - This playlist looks at the GEE plugin for QGIS 425 | - [Handling and analysing vector and raster data cubes with R](https://www.youtube.com/watch?v=9by7zsGms40) - Edzer Pebesma (Institute for Geoinformatics, University of Münster) Summary: vector and raster data cubes include vector and raster data as special cases, but extend this to vector time series, OD matrices, multi-band raster data, multi-band raster time series, multi-attribute vector or raster time series, and more general to array data where one ore more dimensions are associated with space and/or with time. Examples come from pretty much all areas dealing with spatiotemporal data. This tutorial will go through a large number of examples to illustrate this idea, mostly focusing on the packages stars and sf and those supporting their classes (like tmap, mapview, gstat, ggplot2). 426 | - [QiushengWu's youtube](https://www.youtube.com/c/QiushengWu) - This youtube channel has pretty much everything you need Earth Engine, git, colab, Python, Geoscience. Highest quality stuff. 427 | - [The OpenDataCube Conference 2021](https://www.youtube.com/playlist?list=PLlZzWSPAR5GbGTRR68XDKPonOL8dOyYB5) - Playlist from the 2021 conference 428 | - [Dask and Geopandas](https://www.youtube.com/watch?v=ZpA9jgSqAkk) - Scalable geospatial data analysis with Dask| Dask Summit 2021 429 | 430 | ## Earth Engine 431 | 432 | `JavaScript` & `Python` & `R` 433 | 434 | Best to start here [Awesome_GEE](https://github.com/giswqs/Awesome-GEE) - A curated list of Google Earth Engine resources. 435 | 436 | - [Earth Engine API](https://github.com/google/earthengine-api) - `Python` and `JavaScript` bindings for calling the Earth Engine API. 437 | - [from GEE to Numpy to Geotiff](https://mygeoblog.com/2017/10/06/from-gee-to-numpy-to-geotiff/) - Use the GEE python api to export your data to numpy and store the result as a geotiff. 438 | - [Google Earth Engine Community](https://github.com/gee-community) - This organization contains content contributed by the Earth Engine developer community. This is not an officially supported Google product. 439 | - [Geo4Good 2019 workshop materials](https://sites.google.com/earthoutreach.org/geoforgood19/agenda/breakout-sessions) - 2019 material javascript and Python to be found here 440 | - [2018 GEE summit - Dublin materials](https://sites.google.com/earthoutreach.org/eeus2018/agenda/session-descriptions) - 2018 material javascript and Python to be found here 441 | - [10 tips for becoming an Earth Engine expert](https://medium.com/google-earth/10-tips-for-becoming-an-earth-engine-expert-b11aad9e598b) - Keiko Nomura shares her 10 favourite tips 442 | - [Earth Engine Developer list](https://groups.google.com/forum/#!forum/google-earth-engine-developers) - registration required 443 | - [Earth Engine Beginner's Cookbook](https://developers.google.com/earth-engine/tutorials/community/beginners-cookbook) - n this tutorial, we will introduce several types of geospatial data, and enumerate key Earth Engine functions for analyzing and visualizing them. This cookbook was originally created as a workshop during Yale-NUS Data 2.0 hackathon, and later updated for Yale GIS Day 2018 and 2019. `JavaScript` 444 | - [Google Earth Engine Repos](https://github.com/topics/earth-engine) - all the repos matching `earth-engine` 445 | - [geetools](https://github.com/fitoprincipe/geetools-code-editor) - A set of tools to use in Google Earth Engine Code Editor `JavaScript` [docs](https://github.com/fitoprincipe/geetools-code-editor/wiki) 446 | - [gee-up](https://github.com/samapriya/geeup) - Simple CLI for Google Earth Engine Uploads [docs](https://pypi.org/project/geeup/) 447 | - [gee_asset_manager](https://github.com/samapriya/gee_asset_manager_addon) - Google Earth Engine Asset Manager with Addons [docs](https://samapriya.github.io/gee_asset_manager_addon/) 448 | - [Planet-GEE_Pipeline](https://github.com/samapriya/Planet-GEE-Pipeline-CLI) -Planet and Google Earth Engine Pipeline Command Line Interface Tool [docs](https://pypi.org/project/ppipe/) 449 | - [GEE code archive](https://github.com/gena/ee-code-editor-archive) - Unsorted archived Earth Engine scripts `JavaScript` 450 | - [Python GEE notebooks](https://github.com/giswqs/earthengine-py-notebooks) - A collection of 360+ Jupyter Python notebook examples for using Google Earth Engine with interactive mapping 451 | - [GEE Map](https://github.com/giswqs/geemap) - A Python package for interactive mapping with Google Earth Engine, ipyleaflet, and ipywidgets 452 | - [cloud frequency app](https://github.com/robintw/CloudFrequencyApp) - CloudFrequency webapp, using Google App Engine `Python` `JavaScript` 453 | - [rgee](https://github.com/r-spatial/rgee) - Google Earth Engine for `R` [docs](https://csaybar.github.io/rgee/) 454 | - [ee-tensorflow-notebooks](https://github.com/gee-community/ee-tensorflow-notebooks) - Repository to place example notebooks for Deep Learning applications with TensorFlow and Earth Engine. 455 | - [remote-sensing-resistance](https://github.com/mikoontz/remote-sensing-resistance) - Does heterogeneity in forest structure make a forest resistant to wildfire? 456 | - [GoogleEarthEngine](https://github.com/evan-delancey/GoogleEarthEngine) - forestry related work 457 | - [ee-jupyter-examples](https://github.com/tylere/ee-jupyter-examples) - Example Jupyter Notebooks, including ones that use the Earth Engine `Python` API 458 | - [jupyterlab-ee](https://github.com/tylere/jupyterlab-ee) - Experiments related to getting JupyterLab and Earth Engine to work together. `Python` 459 | - [EEwPython](https://github.com/csaybar/EEwPython) - A series of Jupyter notebook to learn Google Earth Engine with `Python` 460 | - [GoogleEarthEngine-side-projects](https://github.com/chrieke/GoogleEarthEngine-side-projects) - Google Earth Engine side projects and tutorial scripts `JavaScript` 461 | - [ox_gee_tutorial](https://github.com/tommylees112/ox_gee_tutorial) - Oxford MSc Introduction to Hydrological Applications in Google Earth Engine 462 | - [crop_yield_prediction](https://github.com/JiaxuanYou/crop_yield_prediction) - Crop Yield Prediction with Deep Learning with GEE 463 | - [geecrop](https://github.com/profLewis/geecrop) - Earth Engine-based crop information 464 | - [radiometric-slope-correction](https://github.com/ESA-PhiLab/radiometric-slope-correction) - Radiometric Slope Correction of Sentinel-1 data on Google Earth Engine 465 | - [geebap](https://github.com/fitoprincipe/geebap) - Best Available Pixel (BAP) composite in Google Earth Engine (GEE) using the `Python` API 466 | - [Ecuador_SEPAL](https://github.com/sig-gis/Ecuador_SEPAL) - processing script for Sentinel-2 and Landsat-8 467 | - [geeguide](https://github.com/ndminhhus/geeguide) - Harmonization of Landsat and Sentinel 2 in Google Earth Engine, documentation and scripts 468 | - [EE-Examples](https://github.com/gorelick/EE-Examples) - `Javascript` some (old?) example scripts from Noel Gorelick - lead author [Google Earth Engine: Planetary-scale geospatial analysis for everyone](https://www.sciencedirect.com/science/article/pii/S0034425717302900) 469 | - [global-river-ice-dataset-from-landsat](https://github.com/seanyx/global-river-ice-dataset-from-Landsat) - `Python` (Google Earth Engine), `JavaScript` (Google Earth Engine) and `R` code to extract river ice condition from Landsat satellites, to develop empirical model, and to predict future changes in river ice 470 | - [GEE_Functions](https://github.com/JavierLopatin/GEE_Functions) - A set of functions to work in Google Engine `Javascript` 471 | - [HMS-Smoke](https://github.com/tianjialiu/HMS-Smoke) - HMS Smoke Explorer: To visualize NOAA's Hazard Mapping System (HMS) smoke product `Javascript` 472 | - [Building_Identification_Damage_Assessment](https://github.com/welkinland/Building_Identification_Damage_Assessment) - Building Extraction and Damage Assessment from High Resolution Multi-spectral Images `Python` 473 | - [Fire_Pattern_Analysis_CONUS](https://github.com/welkinland/Fire_Pattern_Analysis_CONUS) - Analysis of fire patterns and drivers in CONUS `Python` 474 | - [Best Available Pixel](https://github.com/saveriofrancini/bap) - Best Available Pixel calculation using Google Earth Engine `Javascript` 475 | - [ee-palettes](https://github.com/gee-community/ee-palettes) - A set of common color palettes for Google Earth Engine 476 | 477 | ## Open Data Cube 478 | 479 | - [Opendatacube](https://github.com/opendatacube) 480 | - [Datacube Core](https://github.com/opendatacube/datacube-core) - Open Data Cube analyses continental scale Earth Observation data through time `Python` `xarray` 481 | - [Datacube OWS](https://github.com/opendatacube/datacube-ows) - Open web services for the Open Data Cube. Supports WMS, WMTS and WCS for any dataset indexed into the ODC `Python` 482 | - [ODC STAC](https://github.com/opendatacube/odc-stac) - A stand-alone Python library that allows the loading of STAC Items into an ODC-compatible Xarray `xarray` `Python` 483 | - [data_cube_notebooks](https://github.com/ceos-seo/data_cube_notebooks) - Jupyter Notebook examples for our Data Cube capable algorithms and functions `Python` 484 | - [Digital Earth Australia Notebooks](https://github.com/GeoscienceAustralia/dea-notebooks) - Repository for Jupyter Notebooks, tools and workflows for continental-scale earth observation/geospatial analysis with Open Data Cube and `xarray` `Python` 485 | - [Digital Earth Africa Sandbox Notebooks](https://github.com/digitalearthafrica/deafrica-sandbox-notebooks) - Extra documentation about using ODC with Jupyter Notebooks with DE Africa-specific examples `xarray` `Python` 486 | - [odc-tools](https://github.com/opendatacube/odc-tools) - ODC features that DEA is experimenting with or prototyping with the intention of being integrated into odc-core in the future 487 | - [datacube-explorer](https://github.com/opendatacube/datacube-explorer) - Web-based exploration of Open Data Cube collections 488 | - [openeo_odc_driver](https://github.com/SARScripts/openeo_odc_driver) - OpenEO processing engine written in `Python` based on OpenDataCube, `Xarray` and `Dask`. 489 | - [geocube](https://github.com/corteva/geocube) - Tool to convert geopandas vector data into rasterized xarray data `Python` 490 | - [odc-sh](https://github.com/sentinel-hub/odc-sh) - Sentinel Hub plugin for Open data cube 491 | - [dea-coastlines](https://github.com/GeoscienceAustralia/dea-coastlines) - Extracting tidally-constrained annual shorelines and robust rates of coastal change from freely available Earth observation data at continental scale 492 | 493 | ## Other Datacube-related Python 494 | 495 | - [Google Earth Engine Python examples](https://github.com/renelikestacos/Google-Earth-Engine-Python-Examples) - Various examples for Google Earth Engine in `Python` using Jupyter Notebook 496 | - [stackstac](https://github.com/gjoseph92/stackstac) - Turn a STAC catalog into a dask-based xarray `Python` 497 | 498 | ## Planetary Computer 499 | 500 | 501 | - [Mircosoft PlanetaryComputer](https://github.com/microsoft/PlanetaryComputer) - Issues, discussions, and information about the Microsoft Planetary Computer 502 | - [reading-stac](https://planetarycomputer.microsoft.com/docs/quickstarts/reading-stac/) - Reading Data from the STAC API 503 | - [PlanetaryComputerExamples](https://github.com/microsoft/PlanetaryComputerExamples) - Examples of using the Planetary Computer `Python` 504 | - [sdk-python](https://github.com/microsoft/planetary-computer-sdk-for-python) - Planetary Computer SDK for `Python` 505 | - [planetary-computer-apis](https://github.com/microsoft/planetary-computer-apis) 506 | - [PlanetaryComputerDataCatalog](https://github.com/microsoft/PlanetaryComputerDataCatalog) - Data catalog for the Microsoft Planetary Computer [website](https://planetarycomputer.microsoft.com/) 507 | - [planetary-computer-deep-dives](https://github.com/TomAugspurger/planetary-computer-deep-dives) - `Python` 508 | - [Sentinel2 on planetary computer](https://github.com/Element84/geo-notebooks/blob/main/notebooks/odc-planetary-computer.ipynb) - notebook explores Sentinel-2 data on Microsoft's Planetary Computer `Python` 509 | - [satio-pc](https://github.com/dzanaga/satio-pc) - Compute Sentinel features on Planetary Computer `Python` 510 | - [gmv planetary computer S2 alerts](https://github.com/globalmangrovewatch/gmw_planetary_computer_s2_alerts) - Repo with the code producing the GMW alerts using the Microsoft Planetary Computer `Python` 511 | - [hottest panchayats kerala](https://github.com/shijithpk/hottest-panchayats-kerala) - Figuring out what the hottest villages in Kerala are with the help of Microsoft's Planetary Computer. `Python` 512 | 513 | ## QGIS and Grass 514 | 515 | - [Qgis Earth Engine Plugin](https://github.com/gee-community/qgis-earthengine-plugin) - Integrates Google Earth Engine and QGIS using Python API 516 | - [QGIS Earth Engine Plugin - installation guide](https://gee-community.github.io/qgis-earthengine-plugin/) 517 | - [grass-dev-py3-pdal](https://github.com/OSGeo/grass/tree/master/docker) - Dockerfile which compiles GRASS GIS 7.9 master with Python 3 and PDAL suppor 518 | - [qgis-plugin-planet](https://github.com/planetlabs/qgis-planet-plugin) - Browse, filter, preview and download Planet Inc imagery in QGIS. `Python` 519 | - [TSTools - archived](https://github.com/ceholden/TSTools) - QGIS2 plugin tools for remote sensing timeseries `Python` 520 | 521 | ## Climate and weather based resources 522 | 523 | These are `Python` resources. Please see [R resources](#resources-for-r) for info on R 524 | 525 | - [s3 tools](https://github.com/maximlamare/s3_tools) - A collection of sentinel 3 processing tools `Python` 526 | - [eumetsat -python](https://github.com/guidocioni/eumetsat-python) - Shows how to read and plot satellite data from EUMETSAT NETCDF files `Python` 527 | - [unidata on GOES-16](https://unidata.github.io/python-gallery/examples/mapping_GOES16_TrueColor.html) - This notebook shows how to make a true color image from the GOES-16 Advanced Baseline Imager (ABI) level 2 data. We will plot the image with matplotlib and Cartopy.`Python` 528 | - [MetPy](https://github.com/Unidata/MetPy) - MetPy is a collection of tools in Python for reading, visualizing and performing calculations with weather data. `Python` 529 | - [MetPy docs](https://unidata.github.io/MetPy/latest/)`Python` 530 | - [aqua-monitor](https://github.com/Deltares/aqua-monitor) - Monitoring surface water changes from space at global scale. Also checkout the [app](https://aqua-monitor.appspot.com/) `Python` 531 | - [Ocean Color - Modis](https://github.com/JackieVeatch/ocean_color) - introduction to accessing and plotting ocean color satellite data from MODIS `Python` 532 | - [Climate data science](https://github.com/willyhagi/climate-data-science) - Climate Data Science and Earth Observation with `Python` 533 | - [COST-EUMETSAT-Training](https://github.com/gher-ulg/COST-EUMETSAT-Training) - Material, data and presentations for the COST-EUMETSAT training school 534 | - [eumetsat](https://github.com/openclimatefix/eumetsat) - Tools for downloading and processing satellite images from EUMETSAT 535 | - [coda_eumetsat](https://github.com/nicolaerosca/coda_eumetsat) - Coda Eumetsat (coda.eumetsat.int) client for downloading data 536 | - [ai4eo-forecast](https://gitlab.com/Pablo-DBG/ai4eo-forecast) - Developing an open source library to compare Earth Observation and weather forecast services with the actual measurements and assess the accuracy of the forescast `Python` 537 | 538 | ### EUMETlab 539 | 540 | Such a vast collection of resources that it warrants a sub section within Climate and weather based resources 541 | 542 | - [EUMETlab](https://gitlab.eumetsat.int/eumetlab) - This page contains groups of code repositories that have been made open to the public by EUMETSAT and our collaborators. 543 | - [atmosphere](https://gitlab.eumetsat.int/eumetlab/atmosphere/atmosphere) - LTPy - Learning tool for Python on Atmospheric Composition Data is a Python-based training course on Atmospheric Composition Data. The training course covers modules on data access, handling and processing, visualisation as well as case studies. 544 | - [sentinel-downloader](https://gitlab.eumetsat.int/eumetlab/cross-cutting-tools/sentinel-downloader) - Python-based Sentinel satellite data downloader. This script allows for batch downloading of Sentinel data selected by various criteria include date, location, sensor, child products, flags and more. 545 | - [olci-iop-processor](https://gitlab.eumetsat.int/eumetlab/oceans/ocean-science-studies/olci-iop-processor) - Code to produce Inherent Optical Properties from Level-2 OLCI data. 546 | 547 | ## DEM projects 548 | 549 | - [Tin Terrain](https://github.com/heremaps/tin-terrain) - A command-line tool for converting heightmaps in GeoTIFF format into tiled optimized meshes. 550 | - [TauDEM](https://github.com/dtarb/TauDEM) - Terrain Analysis Using Digital Elevation Models (TauDEM) software for hydrologic terrain analysis and channel network extraction. [Docs](http://hydrology.usu.edu/taudem/taudem5/index.html) 551 | - [DEM.net](https://github.com/dem-net/DEM.Net) - Digital Elevation model library in C#. 3D terrain models, line/point Elevations, intervisibility reports. [Docs](https://elevationapi.com/) 552 | - [Stereo Mapping to create Elevation with Python](https://github.com/cmla/s2p) - Satellite Stereo Pipeline 553 | - [DSM2DTM](https://github.com/mprakhar/DSM2DTM) - Code for the paper - Comparison of Digital Building Height Models Extracted from AW3D, TanDEM-X, ASTER, and SRTM Digital Surface Models over Yangon City `Python` 554 | - [The Stereo Pipeline (NASA)](https://ti.arc.nasa.gov/tech/asr/groups/intelligent-robotics/ngt/stereo/) - The NASA Ames Stereo Pipeline (ASP) is a suite of free and open source automated geodesy and stereogrammetry tools designed for processing stereo imagery captured from satellites 555 | 556 | ## SAR 557 | 558 | - [SAR docker](https://github.com/mortcanty/SARDocker) - Source files for Docker image mort/sardocker/ 559 | - [awesome SAR](https://github.com/lveci/awesome-sar) - A curated list of awesome Synthetic Aperture Radar (SAR) software, libraries, and resources. 560 | - [pyroSAR](https://github.com/johntruckenbrodt/pyroSAR) - framework for large-scale SAR satellite data processing 561 | - [PyRAT](https://github.com/birgander2/PyRAT) - General purpose Synthetic Aperture Radar (SAR) postprocessing software package `Python` 562 | - [RITSAR](https://github.com/dm6718/RITSAR) - Synthetic Aperture Radar (SAR) Image Processing Toolbox for `Python` 563 | - [PySAR](https://github.com/bminchew/PySAR) - PyAR is a perpetually incomplete, general-purpose toolbox for common post-processing tasks involving synthetic aperture radar (SAR).`Python` `C++` 564 | - [sarbian](https://github.com/EO-College/sarbian) - a plug’n play Operation System (based on Debian Linux) with all the freely and openly available SAR processing software 565 | - [OpeSARToolkit](https://github.com/ESA-PhiLab/OpenSarToolkit) - High-level functionality for the inventory, download and pre-processing of Sentinel-1 data in the `python` language. 566 | - [infrastructure](https://github.com/ESA-PhiLab/infrastructure) - Mapping and monitoring of infrastructure in desert regions with Sentinel-1 567 | - [OST_Notebook](https://github.com/ESA-PhiLab/OST_Notebooks) - The notebooks within this repository provide getting started tutorials for the use of the Open SAR Toolkit, found here in the ESA-philab github channel. 568 | - [S1_ARD](https://github.com/johntruckenbrodt/S1_ARD) - repository for testing analysis-readiness of Sentinel-1 RTC backscatter `Python` 569 | - [sea_ice_drift](https://github.com/nansencenter/sea_ice_drift) - Sea ice drift from Sentinel-1 SAR imagery using open source feature tracking `Python` 570 | - [s1prepro](https://github.com/benjimin/s1prepro) - Automated pre-processing of Sentinel 1 (satellite radar imagery) `Python` 571 | - [Spacenet6 - SAR buildings](https://github.com/SpaceNetChallenge/SpaceNet_SAR_Buildings_Solutions) - The winning solutions for the SpaceNet 6 Challenge `Python` 572 | - [sentinel1-opds](https://github.com/earthobservatory/sentinel1-opds) - sentinel1-opds ingestion `Python` 573 | - [rice_sentinel1](https://github.com/AndrewPham9/rice_sentinel1) - classify rice from sentinel 1 data `Python` 574 | - [sentineldenoised](https://github.com/nansencenter/sentinel1denoised) - Thermal noise subtraction, scalloping correction, angular correction `Python` 575 | - [sentinel1-Biodiversity](https://github.com/So-YeonBae/Sentinel1-Biodiversity) - Code, example dataset, and instructions of Sentinel-1 data pre-processing and pixel-based summary statistics used in "Radar vision for mapping forest biodiversity from space" `Python` 576 | - [Step by step: Radar-based flood mapping with Python](https://un-spider.org/advisory-support/recommended-practices/recommended-practice-flood-mapping/python-step-by-step) and [github link](https://github.com/UN-SPIDER/radar-based-flood-mapping) - This repository contains a Jupyter Notebook for automatic flood extent mapping using space-based information. `Python` 577 | - [STAC Sentinel1](https://github.com/stactools-packages/sentinel1) - stactools package for working with sentinel1 data `Python` 578 | - [sarsen](https://github.com/bopen/sarsen) - Algorithms and utilities for Synthetic Aperture Radar (SAR) sensors 579 | - [S1_NRB](https://github.com/SAR-ARD/S1_NRB) - A prototype processor for the Sentinel-1 Normalised Radar Backscatter product. 580 | 581 | ## LiDAR 582 | 583 | - [ICESAT extraction script](https://gist.github.com/bzgeo/950f3db986b3513311ed42efe2395171) - Python script to convert from ICESat-2 ATL08 HDF data to shapefile. Usage: 'python icesat2_shp.py 584 | - [ICESAT tools](https://github.com/icesat-2UT/PhoREAL) - Tools and code for Icesat-2 data analysis (Python) 585 | - [usgs-lidar](https://github.com/hobu/usgs-lidar) - AWS Entwine Point Tiles USGS LiDAR Public Dataset GitHub repo 586 | - [Lidar](https://github.com/giswqs/lidar) - Terrain and hydrological analysis based on LiDAR-derived digital elevation models (DEM) 587 | - [IcePyx](https://github.com/icesat2py/icepyx) - Python tools for obtaining and working with ICESat-2 data 588 | 589 | ### GEDI 590 | 591 | - [pyGEDI](https://github.com/EduinHSERNA/pyGEDI) - pyGEDI is a Python Package for NASA's Global Ecosystem Dynamics Investigation (GEDI) mission, data extraction, analysis, processing and visualization. 592 | - [GEDI extraction script](https://gist.github.com/KMarkert/c68ccf53260d7b775b836bf2e11e2ec3) - Python script to take GEDI level 2 data and convert variables to a geospatial vector format 593 | - [rGEDI](https://github.com/carlos-alberto-silva/rGEDI) - rGEDI: An R Package for NASA's Global Ecosystem Dynamics Investigation (GEDI) Data Visualization and Processing. 594 | - [pysl4land](https://github.com/remotesensinginfo/pysl4land) - `Python` tools to process spaceborne lidar (GEDI and ICESAT2) for land (pySL4Land) applications 595 | - [gedi](https://github.com/rodolfolotte/gedi) - `Python` tutorial to process and handle LiDAR GIDE datasets 596 | - [sprnca_gedi](https://github.com/rbavery/sprnca_gedi) - WIP to map Foliage Height Diversity along the San Pedro Riparian Corridor with NASA's GEDI Lidar `Python` 597 | - [GEDI_Yucatan](https://github.com/JohMast/GEDI_Yucatan) - Supplementary material for the study: Space Lidar for Archaeology? Reanalyzing GEDI Data for Detection of Ancient Maya Buildings `R` 598 | - [q_research](https://github.com/HeatherKmtb/q_research) - For processing of ICESat GLAS, GEDI and ICESat-2 LiDAR data, to derive q parameter for canopy height to density relationship `Python` 599 | - [gedi-tutorials](https://github.com/ornldaac/gedi_tutorials) - GEDI L3 and L4 Tutorials 600 | 601 | ## InSAR 602 | 603 | - [ISCE](https://github.com/isce-framework/isce3) - InSAR Scientific Computing Environment version 3 alpha 604 | - [LiCSBAS](https://github.com/yumorishita/LiCSBAS) - LiCSBAS package to carry out InSAR time series analysis using LiCSAR products 605 | - [MintPy](https://github.com/insarlab/MintPy) - Miami InSAR time-series software in Python 606 | - [Pyrocko](https://pyrocko.org/) - Can be utilized flexibly for a variety of geophysical tasks, like seismological data processing and analysis, modelling of InSAR, GPS data and dynamic waveforms, or for seismic source characterization. 607 | - [InSARFlow](https://github.com/levuvietphong/InSARFlow) - Parallel InSAR processing for Time-series analysis 608 | - [PyRate](https://github.com/GeoscienceAustralia/PyRate) - A Python tool for estimating velocity and time-series from Interferometric Synthetic Aperture Radar (InSAR) data. 609 | - [ARIRA-tools](https://github.com/aria-tools/ARIA-tools) - Tools for exploiting ARIA standard products `Python` 610 | - [ISCE_utils](https://github.com/EJFielding/ISCE_utils) - Small utility scripts for working with InSAR Scientific Computing Environment (ISCE) products `Python` 611 | - [ROI_PAC-Sentinel1](https://github.com/RaphaelGrandin/ROI_PAC-Sentinel1) - InSAR processing of Sentinel-1 using ROI_PAC 612 | - [insar_scripts](https://github.com/scottyhq/insar_scripts) - Useful scripts for working with roipac data `Python` 613 | - [isce2](https://github.com/isce-framework/isce2) - InSAR Scientific Computing Environment version 2 `Python` 614 | - [snap2stamps](https://github.com/mdelgadoblasco/snap2stamps) - Using SNAP as InSAR processor for StaMPS 615 | 616 | ## Landuse 617 | 618 | - [demeter](https://github.com/JGCRI/demeter) - A land use land cover disaggregation and change detection model `Python` 619 | 620 | ## Visualisation 621 | 622 | - [Tiled video!](http://gena.github.io/experiments/mapbox/debug/tiled-video-no2.html) 623 | - [Video map](https://github.com/openearth/videomap) - Tools to create, , export and share video maps 624 | - [Tree height and canopy cover map in 3D](https://github.com/nkeikon/GEDI-experiment) - Use Kepler.gl to visualise 3D and 2D data 625 | - [Greppo](https://github.com/greppo-io/greppo) - Python framework for building geospatial web-applications 626 | 627 | ## Regular blogs of significant interest or posts of interest 628 | 629 | - [Philipp Gartner blog](https://philippgaertner.github.io/) 630 | - [Series Temporelles](https://labo.obs-mip.fr/multitemp/) 631 | - [The downlinq](https://medium.com/the-downlinq) 632 | - [GEDI canopy data](https://medium.com/@abt0020/extracting-canopy-height-with-gedi-data-5af8c87df158) - How we processed data to retrieving canopy height 633 | 634 | ## EO code Competitions 635 | 636 | - [challenges 2020](https://github.com/esowc/challenges_2020) - ECMWF Summer of Weather Code 2020 challenges 637 | - [challenges 2021](https://github.com/esowc/challenges_2021) - ECMWF Summer of Weather Code 2021 challenges 638 | - [Julia Wagemann github](https://github.com/jwagemann) - Making open meteorological and climate data better accessible. `Python`, `Jupyter` and `R`. 639 | - See also [Sentinel hub competitions](https://www.sentinel-hub.com/develop/community/contest/) 640 |
'older' competitions of note
641 | - [Planet: Understanding the Amazon from Space](https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/overview) - Use satellite data to track the human footprint in the Amazon rainforest 642 | - [DeepGlobe Building Extraction Challenge](https://competitions.codalab.org/competitions/18544) - We would like to pose the challenge of automatically detecting buildings from satellite images. 643 | - [DSTL feature extraction](https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection) - Kagglers are challenged to accurately classify features in overhead imagery 644 | - [crowdAI misisng maps challenge](https://www.aicrowd.com/challenges/mapping-challenge) - Building Missing Maps with Machine Learning 645 | - [openAI solution](https://github.com/neptune-ai/open-solution-mapping-challenge) - Open solution to the Mapping Challenge 646 | - [AtmosHack2018](https://github.com/wekeo/AtmosHack2018) - Contains information and resources for Copernicus Hackathon 2018 in Helsinki 647 | - [drivendataorg - cloud-cover-winners](https://github.com/drivendataorg/cloud-cover-winners) - Code from the winning submissions for the On Cloud N: Cloud Cover Detection Challenge 648 | 649 | ## ARD links 650 | 651 | - [S1_S2_ARD_code_list](https://github.com/jncc/s1-s2-ard-code-list) - A curated list supporting the use of Sentinel-1 and Sentinel-2 analysis-ready data (ARD) via application programming interface (API) 652 | 653 | ## Useful EO code based twitter accounts 654 | 655 | - [pyGEDI](https://twitter.com/pyGEDI) - pyGEDI is a Python Package for NASA's Global Ecosystem Dynamics Investigation (GEDI) mission, data extraction, analysis, processing and visualization. 656 | 657 | ## Great Github accounts 658 | 659 | Please do explore these accounts, there are some absolutely brilliant projects on these accounts. This was previously a section containing examples, but these are better grouped into the other headings and repitition of links removed. However I feel its very important to highlight individuals wherever possible, ordered by github account name. 660 | 661 | | [Chis Holden](https://github.com/ceholden) | [Christoph Rieke](https://github.com/chrieke) | [gena](https://github.com/gena) | [jgomezdans](https://github.com/jgomezdans) - [blog](http://jgomezdans.github.io/) | [Johntruckhenbrodt](https://github.com/johntruckenbrodt) | [Marcus Netler](https://github.com/neteler) | [Oliverhagolle](https://github.com/olivierhagolle) | [PerryGeo](https://github.com/perrygeo) | [giswqs - Qiusheng Wu](https://github.com/giswqs) | [rhammell](https://github.com/rhammell) | [Remote pixel](https://github.com/RemotePixel) | [robintw](https://github.com/robintw) | [Evan Roualt](https://github.com/rouault) | [samapriya](https://github.com/samapriya) | [shakasom](https://github.com/shakasom) | [yannforget](https://github.com/yannforget) | [Pete Bunting](https://github.com/petebunting) | [Vincent Sarago](https://github.com/vincentsarago) | 662 | 663 | ## EO Geospatial companies or orgs making big contributions 664 | 665 | Github accounts only with examples of work. This section used to contain examples of work, these have been now regrouped into other sections to make them easier to find. 666 | 667 | | [development seed](https://github.com/developmentseed) | [mapbox](https://github.com/mapbox) | [Planet Labs, now just Planet](https://github.com/planetlabs) | [Digital Globe - now Maxar](https://github.com/DigitalGlobe) | [Azavea](https://github.com/azavea) | [Radiant Earth foundation](https://github.com/radiantearth) | [Sentinel Hub](https://github.com/sentinel-hub) | [PyTroll](https://github.com/pytroll) | [CosmiQ](https://github.com/CosmiQ) | [Theia software and tools](https://www.theia-land.fr/en/softwares-and-tools/) | [sparkgeo](https://github.com/sparkgeo) | [Geoscience Australia](https://github.com/GeoscienceAustralia) | [Dymaxion Labs](https://github.com/dymaxionlabs) | [Satellogic](https://github.com/satellogic) | [senbox-org](https://github.com/senbox-org) | [Nasa-gibs](https://github.com/nasa-gibs) | [mundialis](https://github.com/mundialis) | [ESA_PhiLab](https://github.com/ESA-PhiLab) | [Element 84](https://github.com/Element84) 668 | 669 | ## Interesting Non EO parts Python 670 | 671 | This bit could potentially become the most valuable resource. Lets not ignore other sectors/industries/data science, instead lets embrace it and learn from all that other amazing stuff! This my prelude to saying we are earth data scientists 672 | 673 | - [realtime covid19 graphs in USA](https://github.com/k-sys/covid-19) - A collection of work related to COVID-19 674 | - [Deep learning with Python notebooks](https://github.com/fchollet/deep-learning-with-python-notebooks) - Jupyter notebooks for the code samples of the book "Deep Learning with Python" 675 | - [Python data science handbook](https://jakevdp.github.io/PythonDataScienceHandbook/) 676 | - [A-Z of tips and tricks for Python](https://www.freecodecamp.org/news/an-a-z-of-useful-python-tricks-b467524ee747/) - 'Most of these ‘tricks’ are things I’ve used or stumbled upon during my day-to-day work. ' 677 | - [Visual intro into Numpy](https://jalammar.github.io/visual-numpy/)- Visualizing machine learning one concept at a time 678 | - [Change your Jupyter Theme](https://github.com/dunovank/jupyter-themes) - Custom Jupyter Notebook Themes 679 | - [Awesome Semantic Segmentation](https://github.com/mrgloom/awesome-semantic-segmentation) - awesome-semantic-segmentation 680 | - [unidata Python workshop](https://unidata.github.io/python-training/workshop/workshop-intro/) - Would you like some in-depth training on the scientific Python ecosystem for atmospheric science and meteorology? Work through our workshop materials at your own pace to learn and practice the syntax, functionality, and utility of this powerful programming language, or return to the material after taking the workshop in-person to further your understanding of the material you were taught. 681 | - [TernausNet - used in DSTL kaggle competition (came 3rd)](https://github.com/ternaus/TernausNet) - UNet model with VGG11 encoder pre-trained on Kaggle Carvana dataset 682 | - [Introduction to Python for computational science](https://github.com/fangohr/introduction-to-python-for-computational-science-and-engineering) - Book: Introduction to Python for Computational Science and Engineering 683 | - [Another Book on Data Science](https://www.anotherbookondatascience.com/) - Learn R and Python in Parallel 684 | - [Xarray](https://github.com/pydata/xarray) - N-D labeled arrays and datasets in Python 685 | - [Matplotlib colab notebook tutorial](https://colab.research.google.com/github/ageron/handson-ml2/blob/master/tools_matplotlib.ipynb#scrollTo=gG7Fh4EMV2ey) - This notebook demonstrates how to use the matplotlib library to plot beautiful graphs. 686 | - [PostGIS raster cheatsheet](http://www.postgis.us/downloads/postgis20_raster_cheatsheet.pdf) - Useful tips on rasters in PostGIS 687 | - [65 data science books on Springer](https://towardsdatascience.com/springer-has-released-65-machine-learning-and-data-books-for-free-961f8181f189) - not checked but perhaps useful 688 | - [Intro to Numerical Computing - youtube](https://www.youtube.com/watch?v=V0D2mhVt7NE) - Intro to Numerical Computing with NumPy (Beginner) | SciPy 2018 Tutorial | Alex Chabot-Leclerc 689 | - [Classification-Algorithm](https://github.com/usmanr149/Classification-Algorithm) - Classification algorithm workshop for WiMLDS `Python` 690 | - [dtreeviz](https://github.com/parrt/dtreeviz) - A `Python` library for decision tree visualization and model interpretation. 691 | - [Python_tips](https://github.com/gpetepg/python_tips) - Some Python tips for beginner to intermediate users. Also used as a personal cheat sheet. 692 | - [introduction to ml with Python](https://github.com/amueller/introduction_to_ml_with_python) - Notebooks and code for the book "Introduction to Machine Learning with `Python`" 693 | - [Matplotlib_Cheatsheet](https://nbviewer.jupyter.org/urls/gist.githubusercontent.com/Jwink3101/e6b57eba3beca4b05ec146d9e38fc839/raw/f486ca3dcad44c33fc4e7ddedc1f83b82c02b492/Matplotlib_Cheatsheet) - Matplotlib_Cheatsheet `Python` 694 | - [GDSL-UL/Teaching_Links](https://github.com/GDSL-UL/Teaching_Links) - In this repo we have aimed to provide links to useful teaching resources for teaching Geographic / Spatial Data Science, GIS and Statistics. (perhaps misplaced in this list?) 695 | - [practical-python](https://dabeaz-course.github.io/practical-python/) - Practical Python Programming A course by @dabeaz 696 | - [GeoStats, Resources](https://github.com/GeostatsGuy/Resources/blob/master/README.md) - Geostatistics 697 | 698 | ## Interesting Non EO parts other languages 699 | 700 | This section is aimed more a data science/programming resources that 'might' be applicable to Earth Observation, that are not Python 701 | 702 | - [Efficient R programming](https://csgillespie.github.io/efficientR/) - This is the online version of the O’Reilly book: Efficient R programming. Code is [here](https://github.com/csgillespie/efficientR) 703 | 704 | ## Data 705 | 706 | I don't really want to add many data resources to this list as it creeps out of scope but this part contains some good data links [not necessarily EO] 707 | 708 | - [Environmental_Intelligence](https://github.com/rockita/Environmental_Intelligence) - Data for Environmental Intelligence: A mega list of Earth System Datasets covering earth observations, climate, water, forests, biodiversity, ecology, protected areas, natural hazards, marine and the tracking of UN's Sustainable Development Goals 709 | - [gibs](https://earthdata.nasa.gov/eosdis/science-system-description/eosdis-components/gibs) - This is EO 710 | - [awesome-gee-community-datasets](https://samapriya.github.io/awesome-gee-community-datasets/) - Community Datasets added by users and made available for use at large 711 | 712 | ## A footnote on awesome 713 | 714 | There are many awesome lists relating to 'Geo'. I use that term as widely as possible. This list is not meant to replace these lists. Earth Observation is still way behind the GIS world in terms of audience, reach, number of users etc. Things are changing though, by bringing these links together I hope you can see that there has been so much progress in the last 5 years. I do hope these links are helpful espcially to those who are new to Earth Observation, but also to people like me who with several years of experience think they may have seen it all - we haven't and there is still so much to learn. Earth Observation is not just an academic 'thing' or a basemap anymore, it forms the basis for a growing and diverse business environment. Lets embrace this. 715 | 716 | Finally, I wanted to acknowledge a couple of awesome Earth Observation lists that you may list to check out: 717 | 718 | - [Awesome Sentinel](https://github.com/Fernerkundung/awesome-sentinel) - curated list of awesome tools, tutorials and APIs for Copernicus Sentinel satellite data 719 | - [awesome-remote-sensing](https://github.com/attibalazs/awesome-remote-sensing) - Collection of Remote Sensing Resources 720 | - [awesome-Geospatial](https://github.com/sacridini/Awesome-Geospatial) - Long list of geospatial tools and resources 721 | - [awesome-remote-sensing-change-detection](https://github.com/wenhwu/awesome-remote-sensing-change-detection) - List of datasets, codes, and contests related to remote sensing change detection. 722 | - [Awesome Geospatial Companies](https://github.com/chrieke/awesome-geospatial-companies) - List of 500+ geospatial companies (GIS, Earth Observation, UAV, Satellite, Digital Farming, ..) 723 | 724 | #### End 725 | 726 | [![CC BY 1.0][cc-by-shield]][cc-by] 727 | 728 | This work is licensed under a 729 | [Creative Commons Attribution 1.0 International License][cc-by]. 730 | 731 | [![CC BY 1.0][cc-by-image]][cc-by] 732 | 733 | [cc-by]: http://creativecommons.org/licenses/by/1.0/ 734 | [cc-by-image]: https://i.creativecommons.org/l/by/1.0/88x31.png 735 | [cc-by-shield]: https://img.shields.io/badge/License-CC%20BY%201.0-lightgrey 736 | -------------------------------------------------------------------------------- /contributing.md: -------------------------------------------------------------------------------- 1 | # Please feel free to contribute! 2 | Open a pull request 3 | 4 | 1. Fork it 5 | 2. Clone it to your local system 6 | 3. Make a new branch 7 | 4. Make your changes 8 | 5. Push it back to your repo 9 | 6. Click the Compare & pull request button 10 | 7. Click Create pull request to open a new pull request 11 | 12 | Or file an issue 13 | 14 | Or send me an email info@acgeospatial.co.uk 15 | 16 | Or DM me on twitter [map_andrew](https://www.twitter.com/map_andrew) 17 | 18 | Together we are greater than the sum of our parts 19 | 20 | ### New to github or readme.md editing? Then checkout this cheatsheet for help 21 | https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet 22 | 23 | ### Acknowledgements of the people/accounts who have helped with this repo, with how helped in brackets (not ordered) 24 | Thankyou to you all! 25 | - Alastair Graham (lunch time meeting / Pull request) 26 | - Emil Cherrington (lunch time meeting) 27 | - Hayley Evers King (lunch time meeting) 28 | - Xu Teo (lunch time meeting) 29 | - Sam Bancroft (lunch time meeting) 30 | - Gennadii Donchyts (lunch time meeting) 31 | - Qiusheng Wu (Pull request) 32 | - u/Fenr-i-r (link on Reddit) 33 | - u/Jirokoh (link on Reddit) 34 | - [@OHagolle](https://twitter.com/OHagolle) (link on twitter) 35 | - [@Sophie_Villerot](https://twitter.com/Sophie_Villerot) (link on twitter) 36 | - [@gartn001](https://twitter.com/gartn001) (link on twitter) 37 | - [@mustuner2](https://twitter.com/mustuner2) (suggestion of InSAR section and several Python InSAR links on twitter) 38 | - Robbi Bishop-Taylor (Pull request) 39 | - Keiko Nomura (Pull request) 40 | - Fernerkundung (Kersten) (Pull request) 41 | - [@abevingtona](https://twitter.com/abevingtona) (multiple `R` link via twitter) 42 | - Roy Mendelssohn (via email) (rerddap) 43 | - Christoph Rieke (Pull request) 44 | - Emily Selwood (Pull request) 45 | - Riccardo (Pull request) 46 | - [@burdGIS](https://twitter.com/burdGIS) - added QGIS youtube GEE video playlist 47 | - [Dahn Janh](https://twitter.com/DahnJahn) - pull request and also pointed me at [eo books](https://www.eoa.org.au/earth-observation-textbooks) 48 | - Marcus Neteler (neteler) - pull request 49 | - Peter Thaleikis (spekulatius) - pull request - spotted a dead link 50 | - Oscar Baruffa (https://twitter.com/OscarBaruffa) - some R books on geospatial 51 | - Mor Ndiaye (https://twitter.com/papitau) - Several links on R and EO 52 | - Jérôme Gasperi (https://twitter.com/jrom) - resto link 53 | - Julien Osman (https://github.com/Julien-Osman) - Pull request 54 | - Dan Hirst (https://twitter.com/danhirstspace) - Pull request (danhirstos) 55 | - Richard Scott (RichardScottOZ) (Pull request) 56 | - Maxime Liquet (maximlt) (Pull request) 57 | - Alex Leith (Pull Request) - Opendatacubes 58 | - Florian (https://github.com/fwfichtner) - pull request 59 | - Cesar Aybar (https://github.com/csaybar) - pull request 60 | - Luis Lopez (https://github.com/betolink) - pull request 61 | - Scott Staniewicz (https://github.com/scottstanie) - pull request 62 | - Rémi Braun (https://github.com/remi-braun) - pull request 63 | - Gennadii Donchyts (https://twitter.com/gena_d) - pointed me at ee-palettes 64 | - Marco Wolsza (https://github.com/maawoo) - pull request 65 | -------------------------------------------------------------------------------- /create-bookmarks-from-readme/2020-04-17-awesome-eo-code-bookmarks.html: -------------------------------------------------------------------------------- 1 | 2 | 5 | 6 | Awesome-EO-code-Bookmarks 7 |

EO-Bookmarks

8 |

9 |

https://github.com/EduinHSERNA/pyGEDI 10 |
https://gist.github.com/KMarkert/c68ccf53260d7b775b836bf2e11e2ec3 11 |
https://github.com/carlos-alberto-silva/rGEDI 12 |
https://gist.github.com/bzgeo/950f3db986b3513311ed42efe2395171 13 |
https://github.com/icesat-2UT/PhoREAL 14 |
https://github.com/earthlab/earthlab.github.io 15 |
https://github.com/acgeospatial 16 |
https://github.com/acgeospatial/Satellite_Imagery_Python 17 |
https://github.com/acgeospatial/Geospatial_Python_CourseV1 18 |
https://github.com/patrickcgray/open-geo-tutorial 19 |
https://www.gis.usu.edu/~chrisg/python/2009/ 20 |
https://github.com/gee-community/qgis-earthengine-plugin 21 |
https://gee-community.github.io/qgis-earthengine-plugin/ 22 |
https://github.com/mortcanty/SARDocker 23 |
https://github.com/georust 24 |
https://github.com/yeesian/ArchGDAL.jl 25 |
https://github.com/chrieke/awesome-satellite-imagery-datasets 26 |
https://github.com/jensleitloff/CNN-Sentinel 27 |
https://github.com/robmarkcole/satellite-image-deep-learning 28 |
https://github.com/Vooban/Smoothly-Blend-Image-Patches 29 |
https://forums.fast.ai/t/geospatial-deep-learning-resources-study-group/31044 30 |
https://github.com/meet-sapu/Crop-Yield-Prediction-Using-Satellite-Imagery 31 |
https://github.com/Lichtphyz/Houston_flooding 32 |
https://github.com/kkgadiraju/SatelliteImageClassification 33 |
https://github.com/motokimura/spacenet_building_detection 34 |
https://github.com/Paulymorphous/Road-Segmentation 35 |
https://github.com/WarrenGreen/srcnn 36 |
https://github.com/Geoyi/pixel-decoder 37 |
https://github.com/ucalyptus/Detecting-Ships 38 |
https://github.com/trailbehind/DeepOSM 39 |
https://github.com/openearth/videomap 40 |
https://philippgaertner.github.io/ 41 |
https://labo.obs-mip.fr/multitemp/ 42 |
https://www.theia-land.fr/en/softwares-and-tools/ 43 |
https://github.com/shakasom 44 |
https://github.com/shakasom/Deep-Learning-for-Satellite-Imagery 45 |
https://github.com/RemotePixel 46 |
https://github.com/RemotePixel/remotepixel-api 47 |
https://github.com/neteler 48 |
https://github.com/neteler/grass-dev-py3-pdal 49 |
https://github.com/chrieke 50 |
https://github.com/chrieke/awesome-satellite-imagery-datasets 51 |
https://github.com/Fernerkundung/ 52 |
https://github.com/Fernerkundung/awesome-sentinel 53 |
https://github.com/sentinelsat/sentinelsat 54 |
https://sentinelsat.readthedocs.io/en/stable/ 55 |
https://github.com/dwtkns/gdal-cheat-sheet 56 |
https://pcjericks.github.io/py-gdalogr-cookbook/ 57 |
https://jakobmiksch.eu/post/gdal_ogr/ 58 |
https://www.youtube.com/watch?v=Dgr_d8iEWk4 59 |
https://youtu.be/-XMXNmGRk5c?t=455 60 |
https://twitter.com/pyGEDI 61 |
https://medium.com/@abt0020/extracting-canopy-height-with-gedi-data-5af8c87df158 62 |
https://github.com/nmileva/starfm4py 63 |
https://github.com/kscottz/PythonFromSpace 64 |
https://github.com/cmla/s2p 65 |
https://github.com/craic/count_shelters 66 |
https://github.com/parulnith/Satellite-Imagery-Analysis-with-Python 67 |
https://medium.com/analytics-vidhya/satellite-imagery-analysis-with-python-3f8ccf8a7c32 68 |
https://github.com/carsonluuu/Poverty-Prediction-by-Satellite-Imagery 69 |
https://github.com/rander38/Remote-Sensing-Indices-Derivation-Tool 70 |
https://github.com/iam-mhaseeb/Satellite-Imagery-Analysis-of-Vegetation-in-Southern-Pakistan 71 |
https://github.com/esowc/challenges_2020 72 |
https://mygeoblog.com/2017/10/06/from-gee-to-numpy-to-geotiff/ 73 |
https://github.com/gee-community 74 |
https://sites.google.com/earthoutreach.org/geoforgood19/agenda/breakout-sessions 75 |
https://github.com/giswqs/geemap 76 |
https://github.com/developmentseed 77 |
https://github.com/developmentseed/landsat-util 78 |
https://github.com/developmentseed/geolambda 79 |
https://github.com/mapbox 80 |
https://github.com/mapbox/rasterio 81 |
https://github.com/mapbox/robosat 82 |
https://github.com/planetlabs 83 |
https://github.com/planetlabs/notebooks 84 |
https://github.com/DigitalGlobe 85 |
https://github.com/DigitalGlobe/gbdxtools 86 |
https://github.com/azavea 87 |
https://github.com/azavea/raster-vision 88 |
https://github.com/radiantearth 89 |
https://github.com/radiantearth/stac-spec 90 |
https://github.com/sentinel-hub 91 |
https://github.com/sentinel-hub/eo-learn 92 |
https://github.com/sentinel-hub/custom-scripts 93 |
https://github.com/sentinel-hub/eo-flow 94 |
https://github.com/k-sys/covid-19 95 |
https://github.com/fchollet/deep-learning-with-python-notebooks 96 |
https://jakevdp.github.io/PythonDataScienceHandbook/ 97 |
https://www.freecodecamp.org/news/an-a-z-of-useful-python-tricks-b467524ee747/ 98 |
https://jalammar.github.io/visual-numpy/ 99 |
https://github.com/dunovank/jupyter-themes 100 |
https://github.com/mrgloom/awesome-semantic-segmentation 101 |
https://unidata.github.io/python-training/workshop/workshop-intro/ 102 |
https://github.com/ternaus/TernausNet 103 |
https://github.com/fangohr/introduction-to-python-for-computational-science-and-engineering 104 |

105 | -------------------------------------------------------------------------------- /create-bookmarks-from-readme/2020-06-21-awesome-eo-code-bookmarks.html: -------------------------------------------------------------------------------- 1 | 2 | 5 | 6 | Awesome-EO-code-Bookmarks 7 |

EO-Bookmarks

8 |

9 |

https://openeo.org/ 10 |
https://github.com/Open-EO/openeo-processes 11 |
https://processes.openeo.org/ 12 |
https://github.com/topics/satellite-imagery 13 |
https://github.com/topics/earth-observation 14 |
https://github.com/nmileva/starfm4py 15 |
https://github.com/kscottz/PythonFromSpace 16 |
https://github.com/craic/count_shelters 17 |
https://github.com/parulnith/Satellite-Imagery-Analysis-with-Python 18 |
https://medium.com/analytics-vidhya/satellite-imagery-analysis-with-python-3f8ccf8a7c32 19 |
https://github.com/carsonluuu/Poverty-Prediction-by-Satellite-Imagery 20 |
https://github.com/rander38/Remote-Sensing-Indices-Derivation-Tool 21 |
https://github.com/iam-mhaseeb/Satellite-Imagery-Analysis-of-Vegetation-in-Southern-Pakistan 22 |
https://github.com/jonas-eberle/esa_sentinel 23 |
https://github.com/earthlab/earthpy 24 |
https://earthpy.readthedocs.io/en/latest/ 25 |
https://github.com/locationtech/rasterframes 26 |
https://rasterframes.io/ 27 |
https://github.com/cfranken/SIF_tools 28 |
https://github.com/MarcYin/SIAC 29 |
https://github.com/MarcYin/S2_TOA_TO_LAI 30 |
https://github.com/avanetten/cresi 31 |
https://github.com/rouault/cog_validator 32 |
https://github.com/samsammurphy/6S_emulator 33 |
https://github.com/daleroberts/bv 34 |
https://github.com/ungarj/mapchete 35 |
https://github.com/arthur-e/unmixing 36 |
https://github.com/SatelliteApplicationsCatapult/sedas_pyapi 37 |
https://github.com/JamesOConnor/Sentinel_bot 38 |
https://twitter.com/sentinel_bot 39 |
https://github.com/robintw/Py6S 40 |
https://github.com/robintw/XArray_PyConUK2018 41 |
https://github.com/robintw/PyProSAIL 42 |
https://github.com/gena/gbdx-surface-water 43 |
https://github.com/gena/landsat7-errors 44 |
https://github.com/jgomezdans/get_modis 45 |
https://github.com/jgomezdans/prosail 46 |
https://github.com/yannforget/landsatxplore 47 |
https://github.com/yannforget/pylandsat 48 |
https://github.com/yannforget/landsat-sentinel-fusion 49 |
https://github.com/perrygeo/pyimpute 50 |
https://github.com/rhammell/planet-movement 51 |
https://github.com/olivierhagolle/Sentinel-download 52 |
https://github.com/RemotePixel/remotepixel-api 53 |
https://github.com/sentinelsat/sentinelsat 54 |
https://sentinelsat.readthedocs.io/en/stable/ 55 |
https://github.com/giswqs/whitebox-python 56 |
https://github.com/johntruckenbrodt/spatialist 57 |
https://github.com/olivierhagolle/LANDSAT-Download 58 |
https://github.com/perrygeo/python-rasterstats 59 |
https://github.com/satellogic/orbit-predictor 60 |
https://github.com/developmentseed/landsat-util 61 |
https://github.com/developmentseed/geolambda 62 |
https://github.com/developmentseed/rio-viz 63 |
https://github.com/developmentseed/cogeo-mosaic 64 |
https://github.com/developmentseed/sentinel-2-cog 65 |
https://github.com/developmentseed/sentinel-s3 66 |
https://github.com/mapbox/rasterio 67 |
https://github.com/planetlabs/notebooks 68 |
https://github.com/planetlabs/planet-client-python 69 |
https://github.com/DigitalGlobe/gbdxtools 70 |
https://github.com/azavea/pystac 71 |
https://github.com/radiantearth/stac-spec 72 |
https://github.com/sentinel-hub/sentinelhub-py 73 |
https://github.com/sentinel-hub/sentinel2-cloud-detector 74 |
https://github.com/pytroll/satpy 75 |
https://github.com/pytroll/pyresample 76 |
https://github.com/CosmiQ/CometTS 77 |
https://github.com/sparkgeo/stac-validator 78 |
https://github.com/dymaxionlabs/dask-rasterio 79 |
https://github.com/dymaxionlabs/ap-latam 80 |
https://github.com/satellogic/telluric 81 |
https://github.com/nasa-gibs/onearth 82 |
https://github.com/mundialis/actinia_core 83 |
https://github.com/mundialis/actinia_satellite_plugin 84 |
https://github.com/mikoontz/local-structure-wpb-severity 85 |
https://github.com/acolite/acolite_mr 86 |
https://github.com/corteva/geocube 87 |
https://corteva.github.io/geocube/stable/ 88 |
https://github.com/geospatial-jeff/async-cog-reader 89 |
https://github.com/ceholden/cedar-datacube 90 |
https://ceholden.github.io/cedar-datacube/master/ 91 |
https://github.com/ceholden/stems 92 |
https://ceholden.github.io/stems/master/ 93 |
https://github.com/davidbrochart/ipyearth 94 |
https://github.com/Seyed-Ali-Ahmadi/Python-for-Remote-Sensing 95 |
https://earthobserv.com/ 96 |
https://github.com/Rabscuttler/esda-dissertation 97 |
https://github.com/arthur-e/unmixing 98 |
https://github.com/cvitolo/geff_notebooks 99 |
https://github.com/tgrippa/Opensource_OBIA_processing_chain 100 |
https://github.com/geospatial-jeff/aiocogeo 101 |
https://github.com/makepath/xarray-spatial 102 |
https://github.com/fatiando/verde 103 |
https://github.com/cmla/s2p 104 |
https://github.com/dcs4cop/xcube 105 |
https://github.com/OpenGeoscience/geonotebook 106 |
https://github.com/corteva/rioxarray 107 |
https://corteva.github.io/rioxarray/stable/ 108 |
https://github.com/mapbox/COGDumper 109 |
https://github.com/GeoBigData/tatortot 110 |
https://github.com/DigitalGlobe/tiletanic 111 |
https://automating-gis-processes.github.io 112 |
https://rspatial.org/raster/rs/1-introduction.html 113 |
https://rspatial.org/raster/rs/index.html 114 |
https://cran.r-project.org/web/packages/gdalcubes/index.html 115 |
https://github.com/appelmar/gdalcubes_R 116 |
https://gist.github.com/franzalex/a95e227cab9b146a6092 117 |
https://github.com/giswqs/whiteboxR 118 |
https://github.com/jblindsay/whitebox-tools 119 |
https://cran.r-project.org/web/packages/rasterVis/index.html 120 |
https://cran.r-project.org/web/packages/landsat/index.html 121 |
https://github.com/ropensci/rnoaa 122 |
https://github.com/ropensci/MODISTools 123 |
https://docs.ropensci.org/MODISTools/ 124 |
https://www.tylermw.com/a-step-by-step-guide-to-making-3d-maps-with-satellite-imagery-in-r/ 125 |
https://github.com/jdbcode/LandsatLinkr 126 |
https://github.com/bevingtona/planetR 127 |
https://github.com/andrew-plowright/ForestTools 128 |
https://github.com/Jean-Romain/lidR 129 |
https://github.com/Jean-Romain/lidRplugins 130 |
https://github.com/r-spatial/stars 131 |
https://github.com/16EAGLE/getSpatialData 132 |
https://bleutner.github.io/RStoolbox/ 133 |
https://github.com/MBalthasar/rHarmonics 134 |
https://github.com/ropensci/rerddap 135 |
https://docs.ropensci.org/rerddap/ 136 |
https://upwell.pfeg.noaa.gov/erddap/index.html 137 |
https://github.com/joheisig/Spatial_Data_in_R 138 |
https://github.com/geospatial-jeff/cognition-datasources 139 |
https://github.com/georust 140 |
https://github.com/yeesian/ArchGDAL.jl 141 |
https://github.com/acgeospatial/Julia_Geospatial 142 |
https://geotrellis.io/ 143 |
https://github.com/locationtech/geotrellis 144 |
https://github.com/lukeroth/gdal 145 |
https://github.com/appelmar/gdalcubes 146 |
https://bitbucket.org/petebunting/rsgislib/src/bf7933996822/?at=default 147 |
https://github.com/jblindsay/whitebox-geospatial-analysis-tools 148 |
https://metacpan.org/pod/Geo::GDAL 149 |
https://github.com/PDAL/PDAL 150 |
https://github.com/davidfrantz/force 151 |
https://github.com/jdbcode/LLR-LandTrendr 152 |
https://github.com/Vizzuality/gfw 153 |
https://github.com/yannforget/conda-recipes 154 |
https://github.com/jdbcode/landsat-solar-elevation 155 |
https://github.com/planetlabs/staccato 156 |
https://github.com/azavea/stac4s 157 |
https://github.com/radiantearth/stac-browser 158 |
https://github.com/sentinel-hub/custom-scripts 159 |
https://github.com/sentinel-hub/sentinelhub-js 160 |
https://github.com/senbox-org/s3tbx 161 |
https://github.com/senbox-org/s2tbx 162 |
https://github.com/senbox-org/s1tbx 163 |
https://github.com/senbox-org/snap-engine 164 |
https://github.com/nasa-gibs/worldview 165 |
https://worldview.earthdata.nasa.gov/ 166 |
https://github.com/orfeotoolbox/OTB 167 |
https://gitlab.orfeo-toolbox.org/orfeotoolbox/otb 168 |
https://github.com/ceholden/landsat_preprocess 169 |
https://github.com/m-mohr/stac-node-validator 170 |
https://github.com/vannizhang/aiforearth-landcover-app 171 |
https://github.com/emilyselwood/tiffhax 172 |
https://github.com/earthlab/earthlab.github.io 173 |
https://github.com/EO-College 174 |
https://github.com/EO-College/tomography_tutorial 175 |
https://github.com/profLewis/geog0111 176 |
https://carpentries-incubator.github.io/geospatial-python/ 177 |
https://github.com/acgeospatial 178 |
https://github.com/acgeospatial/Satellite_Imagery_Python 179 |
https://github.com/acgeospatial/Geospatial_Python_CourseV1 180 |
https://github.com/patrickcgray/open-geo-tutorial 181 |
https://github.com/ceholden/open-geo-tutorial 182 |
https://github.com/samfranklin/foss4guk19-jupyter 183 |
https://www.gis.usu.edu/~chrisg/python/2009/ 184 |
https://github.com/planetlabs/training-workshop 185 |
https://github.com/chrieke/awesome-satellite-imagery-datasets 186 |
https://github.com/deepVector/geospatial-machine-learning 187 |
https://github.com/robmarkcole/satellite-image-deep-learning 188 |
https://github.com/ternaus/TernausNetV2 189 |
https://github.com/jensleitloff/CNN-Sentinel 190 |
https://github.com/Vooban/Smoothly-Blend-Image-Patches 191 |
https://forums.fast.ai/t/geospatial-deep-learning-resources-study-group/31044 192 |
https://github.com/meet-sapu/Crop-Yield-Prediction-Using-Satellite-Imagery 193 |
https://github.com/Lichtphyz/Houston_flooding 194 |
https://github.com/kkgadiraju/SatelliteImageClassification 195 |
https://github.com/motokimura/spacenet_building_detection 196 |
https://github.com/Paulymorphous/Road-Segmentation 197 |
https://github.com/WarrenGreen/srcnn 198 |
https://github.com/Geoyi/pixel-decoder 199 |
https://github.com/ucalyptus/Detecting-Ships 200 |
https://github.com/trailbehind/DeepOSM 201 |
https://github.com/MaxLenormand/Keras-for-computer-vision 202 |
https://medium.com/@kylepob61392/airplane-image-classification-using-a-keras-cnn-22be506fdb53 203 |
https://github.com/sshuair/torchsat 204 |
https://torchsat.readthedocs.io/en/latest/ 205 |
https://github.com/esowc/ml_drought 206 |
https://ml-clim.github.io/drought-prediction/ 207 |
https://github.com/gabrieltseng/pycrop-yield-prediction 208 |
https://datapink.io/datapink/neat-EO/ 209 |
https://github.com/lukasliebel/dfc2020_baseline 210 |
https://github.com/rhammell/planesnet 211 |
https://github.com/rhammell/planesnet-detector 212 |
https://github.com/rhammell/shipsnet-detector 213 |
https://github.com/shakasom/Deep-Learning-for-Satellite-Imagery 214 |
https://github.com/nshaud/DeepNetsForEO 215 |
https://github.com/radiantearth/mlhub-tutorials 216 |
https://mlhub.earth/ 217 |
https://github.com/sentinel-hub/eo-learn 218 |
https://github.com/developmentseed/label-maker 219 |
https://github.com/cosmiq/solaris 220 |
https://solaris.readthedocs.io/en/latest/ 221 |
https://github.com/CosmiQ/CosmiQ_SN6_Baseline 222 |
https://github.com/mapbox/robosat 223 |
https://github.com/sentinel-hub/eo-flow 224 |
https://github.com/azavea/raster-vision 225 |
https://github.com/azavea/raster-vision-aws 226 |
https://up42.com/blog/tech/using-tensorboard-while-training-land-cover-models-with-satellite-imagery 227 |
https://github.com/nealjean/predicting-poverty 228 |
https://github.com/darribas/satellite_led_liverpool 229 |
https://github.com/Azure/pixel_level_land_classification 230 |
https://github.com/marcbelmont/satellite-image-object-detection 231 |
https://github.com/dwtkns/gdal-cheat-sheet 232 |
https://pcjericks.github.io/py-gdalogr-cookbook/ 233 |
https://jakobmiksch.eu/post/gdal_ogr/ 234 |
https://github.com/perrygeo/docker-gdal-base 235 |
https://www.youtube.com/watch?v=N_dmiQI1s24 236 |
https://medium.com/planet-stories/a-gentle-introduction-to-gdal-part-1-a3253eb96082 237 |
https://medium.com/planet-stories/a-gentle-introduction-to-gdal-part-2-map-projections-gdalwarp-e05173bd710a 238 |
https://medium.com/planet-stories/a-gentle-introduction-to-gdal-part-3-geodesy-local-map-projections-794c6ff675ca 239 |
https://github.com/azavea/loam 240 |
https://github.com/nasa-gibs/mrf 241 |
https://www.youtube.com/watch?v=Dgr_d8iEWk4 242 |
https://youtu.be/-XMXNmGRk5c?t=455 243 |
https://www.youtube.com/watch?v=j15MryznWn4 244 |
https://www.youtube.com/watch?v=rUUgLsspTZA&t 245 |
https://www.youtube.com/watch?v=OsgZSlv4t-U 246 |
https://www.youtube.com/watch?v=FenT61l-xyQ 247 |
https://www.youtube.com/playlist?list=PLAxJ4-o7ZoPccOFv1dCwvGI6TYnirRTg3 248 |
https://www.youtube.com/watch?v=m1ejxSi3l8s 249 |
https://www.youtube.com/watch?v=fal4Jj81uMA 250 |
https://www.youtube.com/watch?v=3KRYObqpMlk 251 |
https://github.com/giswqs/Awesome-GEE 252 |
https://github.com/google/earthengine-api 253 |
https://mygeoblog.com/2017/10/06/from-gee-to-numpy-to-geotiff/ 254 |
https://github.com/gee-community 255 |
https://sites.google.com/earthoutreach.org/geoforgood19/agenda/breakout-sessions 256 |
https://sites.google.com/earthoutreach.org/eeus2018/agenda/session-descriptions 257 |
https://medium.com/google-earth/10-tips-for-becoming-an-earth-engine-expert-b11aad9e598b 258 |
https://groups.google.com/forum/#!forum/google-earth-engine-developers 259 |
https://developers.google.com/earth-engine/tutorials/community/beginners-cookbook 260 |
https://github.com/topics/earth-engine 261 |
https://github.com/fitoprincipe/geetools-code-editor 262 |
https://github.com/fitoprincipe/geetools-code-editor/wiki 263 |
https://github.com/samapriya/geeup 264 |
https://pypi.org/project/geeup/ 265 |
https://github.com/samapriya/gee_asset_manager_addon 266 |
https://samapriya.github.io/gee_asset_manager_addon/ 267 |
https://github.com/samapriya/Planet-GEE-Pipeline-CLI 268 |
https://pypi.org/project/ppipe/ 269 |
https://github.com/gena/ee-code-editor-archive 270 |
https://github.com/giswqs/earthengine-py-notebooks 271 |
https://github.com/giswqs/geemap 272 |
https://github.com/robintw/CloudFrequencyApp 273 |
https://github.com/r-spatial/rgee 274 |
https://csaybar.github.io/rgee/ 275 |
https://github.com/philippgaertner/Landsat-Project-Cost-Estimator 276 |
https://github.com/gee-community/ee-tensorflow-notebooks 277 |
https://github.com/mikoontz/remote-sensing-resistance 278 |
https://github.com/evan-delancey/GoogleEarthEngine 279 |
https://github.com/tylere/ee-jupyter-examples 280 |
https://github.com/tylere/jupyterlab-ee 281 |
https://github.com/csaybar/EEwPython 282 |
https://github.com/chrieke/GoogleEarthEngine-side-projects 283 |
https://github.com/opendatacube 284 |
https://github.com/opendatacube/datacube-core 285 |
https://github.com/opendatacube/datacube-notebooks 286 |
https://github.com/ceos-seo/data_cube_ui 287 |
https://github.com/ceos-seo/data_cube_utilities 288 |
https://github.com/GeoscienceAustralia/dea-notebooks 289 |
https://github.com/renelikestacos/Google-Earth-Engine-Python-Examples 290 |
https://github.com/gee-community/qgis-earthengine-plugin 291 |
https://gee-community.github.io/qgis-earthengine-plugin/ 292 |
https://github.com/neteler/grass-dev-py3-pdal 293 |
https://github.com/planetlabs/qgis-planet-plugin 294 |
https://github.com/ceholden/TSTools 295 |
https://github.com/maximlamare/s3_tools 296 |
https://github.com/guidocioni/eumetsat-python 297 |
https://unidata.github.io/python-gallery/examples/mapping_GOES16_TrueColor.html 298 |
https://github.com/Unidata/MetPy 299 |
https://unidata.github.io/MetPy/latest/ 300 |
https://github.com/Deltares/aqua-monitor 301 |
https://aqua-monitor.appspot.com/ 302 |
https://github.com/JackieVeatch/ocean_color 303 |
https://github.com/willyhagi/climate-data-science 304 |
https://github.com/heremaps/tin-terrain 305 |
https://github.com/dtarb/TauDEM 306 |
https://github.com/dem-net/DEM.Net 307 |
https://elevationapi.com/ 308 |
https://github.com/cmla/s2p 309 |
https://github.com/mortcanty/SARDocker 310 |
https://github.com/lveci/awesome-sar 311 |
https://github.com/johntruckenbrodt/pyroSAR 312 |
https://github.com/birgander2/PyRAT 313 |
https://github.com/dm6718/RITSAR 314 |
https://github.com/bminchew/PySAR 315 |
https://github.com/EO-College/sarbian 316 |
https://github.com/EduinHSERNA/pyGEDI 317 |
https://gist.github.com/KMarkert/c68ccf53260d7b775b836bf2e11e2ec3 318 |
https://github.com/carlos-alberto-silva/rGEDI 319 |
https://gist.github.com/bzgeo/950f3db986b3513311ed42efe2395171 320 |
https://github.com/icesat-2UT/PhoREAL 321 |
https://github.com/hobu/usgs-lidar 322 |
https://github.com/giswqs/lidar 323 |
https://github.com/isce-framework/isce3 324 |
https://github.com/yumorishita/LiCSBAS 325 |
https://github.com/insarlab/MintPy 326 |
https://pyrocko.org/ 327 |
https://github.com/levuvietphong/InSARFlow 328 |
https://github.com/GeoscienceAustralia/PyRate 329 |
https://github.com/aria-tools/ARIA-tools 330 |
https://github.com/EJFielding/ISCE_utils 331 |
https://github.com/RaphaelGrandin/ROI_PAC-Sentinel1 332 |
https://github.com/scottyhq/insar_scripts 333 |
https://github.com/bakerunavco/https://github.com/bakerunavco/pygmtsar 334 |
https://github.com/isce-framework/isce2 335 |
https://github.com/mdelgadoblasco/snap2stamps 336 |
https://github.com/openearth/videomap 337 |
https://github.com/nkeikon/GEDI-experiment 338 |
https://philippgaertner.github.io/ 339 |
https://labo.obs-mip.fr/multitemp/ 340 |
https://medium.com/the-downlinq 341 |
https://medium.com/@abt0020/extracting-canopy-height-with-gedi-data-5af8c87df158 342 |
https://github.com/esowc/challenges_2020 343 |
https://spacenetchallenge.github.io/ 344 |
https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/overview 345 |
https://competitions.codalab.org/competitions/18544 346 |
https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection 347 |
https://www.crowdai.org/challenges/mapping-challenge 348 |
https://github.com/neptune-ai/open-solution-mapping-challenge 349 |
https://twitter.com/pyGEDI 350 |
https://github.com/ceholden 351 |
https://github.com/chrieke 352 |
https://github.com/Fernerkundung/ 353 |
https://github.com/gena 354 |
https://github.com/jgomezdans 355 |
https://github.com/johntruckenbrodt 356 |
https://github.com/neteler 357 |
https://github.com/olivierhagolle 358 |
https://github.com/perrygeo 359 |
https://github.com/giswqs 360 |
https://github.com/rhammell 361 |
https://github.com/RemotePixel 362 |
https://github.com/robintw 363 |
https://github.com/rouault 364 |
https://github.com/samapriya 365 |
https://github.com/shakasom 366 |
https://github.com/yannforget 367 |
https://github.com/developmentseed 368 |
https://github.com/mapbox 369 |
https://github.com/planetlabs 370 |
https://github.com/DigitalGlobe 371 |
https://github.com/azavea 372 |
https://github.com/radiantearth 373 |
https://github.com/sentinel-hub 374 |
https://github.com/pytroll 375 |
https://github.com/CosmiQ 376 |
https://www.theia-land.fr/en/softwares-and-tools/ 377 |
https://github.com/sparkgeo 378 |
https://github.com/GeoscienceAustralia 379 |
https://github.com/dymaxionlabs 380 |
https://github.com/satellogic 381 |
https://github.com/senbox-org 382 |
https://github.com/nasa-gibs 383 |
https://github.com/mundialis 384 |
https://github.com/k-sys/covid-19 385 |
https://github.com/fchollet/deep-learning-with-python-notebooks 386 |
https://jakevdp.github.io/PythonDataScienceHandbook/ 387 |
https://www.freecodecamp.org/news/an-a-z-of-useful-python-tricks-b467524ee747/ 388 |
https://jalammar.github.io/visual-numpy/ 389 |
https://github.com/dunovank/jupyter-themes 390 |
https://github.com/mrgloom/awesome-semantic-segmentation 391 |
https://unidata.github.io/python-training/workshop/workshop-intro/ 392 |
https://github.com/ternaus/TernausNet 393 |
https://github.com/fangohr/introduction-to-python-for-computational-science-and-engineering 394 |
https://www.anotherbookondatascience.com/ 395 |
https://github.com/pydata/xarray 396 |
https://colab.research.google.com/github/ageron/handson-ml2/blob/master/tools_matplotlib.ipynb#scrollTo=gG7Fh4EMV2ey 397 |
https://towardsdatascience.com/springer-has-released-65-machine-learning-and-data-books-for-free-961f8181f189 398 |
https://www.youtube.com/watch?v=V0D2mhVt7NE 399 |
https://github.com/usmanr149/Classification-Algorithm 400 |
https://github.com/parrt/dtreeviz 401 |
https://github.com/gpetepg/python_tips 402 |
https://github.com/amueller/introduction_to_ml_with_python 403 |
https://csgillespie.github.io/efficientR/ 404 |
https://github.com/csgillespie/efficientR 405 |
https://github.com/luigidifraia/jupyterhub-platform-tutorials 406 |
https://github.com/rockita/Environmental_Intelligence 407 |
https://earthdata.nasa.gov/eosdis/science-system-description/eosdis-components/gibs 408 |
https://github.com/Fernerkundung/awesome-sentinel 409 |
https://github.com/attibalazs/awesome-remote-sensing 410 |
https://github.com/sacridini/awesome-remote-sensing-papers 411 |
https://github.com/sacridini/Awesome-Geospatial 412 |

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EO-Bookmarks

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https://www.eoa.org.au/earth-observation-textbooks 10 |
https://business.esa.int/newcomers-earth-observation-guide 11 |
https://landscape.satsummit.io/ 12 |
https://github.com/topics/satellite-imagery 13 |
https://github.com/topics/earth-observation 14 |
https://openeo.org/ 15 |
https://github.com/Open-EO/openeo-processes 16 |
https://processes.openeo.org/ 17 |
https://remotesensing.info/ 18 |
https://www.rsgislib.org/ 19 |
https://arcsi.remotesensing.info/ 20 |
https://eodatadown.remotesensing.info/ 21 |
https://eodag.readthedocs.io/en/latest/ 22 |
https://github.com/SatelliteApplicationsCatapult/sedas_pyapi 23 |
https://github.com/jonas-eberle/esa_sentinel 24 |
https://github.com/jgomezdans/get_modis 25 |
https://github.com/yannforget/landsatxplore 26 |
https://github.com/yannforget/pylandsat 27 |
https://github.com/olivierhagolle/Sentinel-download 28 |
https://github.com/sentinelsat/sentinelsat 29 |
https://sentinelsat.readthedocs.io/en/stable/ 30 |
https://github.com/olivierhagolle/LANDSAT-Download 31 |
https://github.com/developmentseed/landsat-util 32 |
https://github.com/insarwxw/Sentinel-1_POE_orbit_download 33 |
https://lpdaac.usgs.gov/tools/data-prep-scripts/ 34 |
https://git.earthdata.nasa.gov/projects/LPDUR 35 |
https://nbviewer.jupyter.org/gist/scottyhq/a1ddbb12f97764860160370229b19261 36 |
https://medium.com/pangeo/intake-stac-nasa-4cd78d6246b7 37 |
https://github.com/FrontierDevelopmentLab/sat-extractor 38 |
https://github.com/nmileva/starfm4py 39 |
https://github.com/rander38/Remote-Sensing-Indices-Derivation-Tool 40 |
https://github.com/earthlab/earthpy 41 |
https://earthpy.readthedocs.io/en/latest/ 42 |
https://github.com/locationtech/rasterframes 43 |
https://rasterframes.io/ 44 |
https://github.com/cfranken/SIF_tools 45 |
https://github.com/MarcYin/SIAC 46 |
https://github.com/MarcYin/S2_TOA_TO_LAI 47 |
https://github.com/avanetten/cresi 48 |
https://github.com/samsammurphy/6S_emulator 49 |
https://github.com/daleroberts/bv 50 |
https://github.com/ungarj/mapchete 51 |
https://github.com/arthur-e/unmixing 52 |
https://github.com/yannforget/landsat-sentinel-fusion 53 |
https://github.com/rhammell/planet-movement 54 |
https://github.com/ceholden/cedar-datacube 55 |
https://ceholden.github.io/cedar-datacube/master/ 56 |
https://github.com/ceholden/stems 57 |
https://ceholden.github.io/stems/master/ 58 |
https://github.com/davidbrochart/ipyearth 59 |
https://github.com/Seyed-Ali-Ahmadi/Python-for-Remote-Sensing 60 |
https://earthobserv.com/ 61 |
https://github.com/Rabscuttler/esda-dissertation 62 |
https://github.com/cvitolo/geff_notebooks 63 |
https://github.com/redfoxgis/river-width 64 |
https://github.com/briannapagan/get_river_width/blob/master/get_river_width.py 65 |
https://github.com/redfoxgis/extract_water/blob/master/extract_water.py 66 |
https://github.com/pytroll/pyresample 67 |
https://github.com/johntruckenbrodt/spatialist 68 |
https://github.com/CosmiQ/CometTS 69 |
https://github.com/satellogic/telluric 70 |
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https://github.com/dcs4cop/xcube 77 |
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https://github.com/GeoBigData/tatortot 79 |
https://github.com/DigitalGlobe/tiletanic 80 |
https://automating-gis-processes.github.io 81 |
https://github.com/JamesOConnor/openaq-s5 82 |
https://github.com/tommylees112/vegetation_health 83 |
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https://github.com/plant99/felicette 85 |
https://github.com/kvos/CoastSat 86 |
https://github.com/JavierLopatin/Python-Remote-Sensing-Scripts 87 |
https://github.com/petescarth/fc-up42 88 |
https://github.com/tgrippa/Opensource_OBIA_processing_chain 89 |
https://github.com/nansencenter/nansat 90 |
https://nansat.readthedocs.io/en/latest/index.html 91 |
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https://github.com/DrGuy/ieo 93 |
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https://github.com/EkicierNico/pykic 96 |
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https://github.com/terrascope/gocog 124 |
https://github.com/carsonluuu/Poverty-Prediction-by-Satellite-Imagery 125 |
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https://github.com/craic/count_shelters 127 |
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https://github.com/iam-mhaseeb/Satellite-Imagery-Analysis-of-Vegetation-in-Southern-Pakistan 130 |
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https://twitter.com/sentinel_bot 132 |
https://github.com/dymaxionlabs/ap-latam 133 |
https://github.com/mikoontz/local-structure-wpb-severity 134 |
https://github.com/hfisser/Truck_Detection_Sentinel2_COVID19 135 |
https://github.com/planetlabs/notebooks 136 |
https://github.com/planetlabs/planet-client-python 137 |
https://github.com/DigitalGlobe/gbdxtools 138 |
https://github.com/gena/gbdx-surface-water 139 |
https://github.com/sentinel-hub/sentinelhub-py 140 |
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https://github.com/robintw/PyProSAIL 147 |
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https://github.com/planetlabs/color_balance 152 |
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https://github.com/mapbox/rasterio 154 |
https://github.com/robintw/XArray_PyConUK2018 155 |
https://github.com/perrygeo/python-rasterstats 156 |
https://github.com/pytroll/satpy 157 |
https://github.com/perrygeo/pyimpute 158 |
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https://github.com/giswqs/whitebox-python 165 |
https://github.com/dlr-eoc/ukis-pysat 166 |
https://pypi.org/project/image-similarity-measures/ 167 |
https://pypi.org/project/fake-geo-images/ 168 |
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https://rspatial.org/raster/rs/1-introduction.html 170 |
https://rspatial.org/raster/rs/index.html 171 |
https://cran.r-project.org/web/packages/gdalcubes/index.html 172 |
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https://www.tylermw.com/a-step-by-step-guide-to-making-3d-maps-with-satellite-imagery-in-r/ 182 |
https://github.com/jdbcode/LandsatLinkr 183 |
https://github.com/bevingtona/planetR 184 |
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https://github.com/Jean-Romain/lidR 186 |
https://github.com/Jean-Romain/lidRplugins 187 |
https://github.com/r-spatial/stars 188 |
https://github.com/16EAGLE/getSpatialData 189 |
https://bleutner.github.io/RStoolbox/ 190 |
https://github.com/MBalthasar/rHarmonics 191 |
https://github.com/ropensci/rerddap 192 |
https://docs.ropensci.org/rerddap/ 193 |
https://upwell.pfeg.noaa.gov/erddap/index.html 194 |
https://github.com/joheisig/Spatial_Data_in_R 195 |
https://github.com/geospatial-jeff/cognition-datasources 196 |
https://github.com/ecmwf/caliver 197 |
https://github.com/lecrabe/clip_time_series 198 |
https://github.com/spatialstatisticsupna/RGISTools 199 |
https://github.com/JavierLopatin/Grassland-Species-Classification 200 |
https://github.com/JavierLopatin/UAV-InvasiveSpp 201 |
https://github.com/JavierLopatin/Peatland-carbon-stock 202 |
https://github.com/JavierLopatin/SpeciesRichness-GLMvsRF-LiDAR 203 |
https://github.com/redfoxgis/tree_segmentation 204 |
https://github.com/be-marc/swdt 205 |
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https://urbanspatial.github.io/classifying_satellite_imagery_in_R/ 207 |
https://github.com/georust 208 |
https://github.com/yeesian/ArchGDAL.jl 209 |
https://github.com/acgeospatial/Julia_Geospatial 210 |
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https://github.com/locationtech/geotrellis 212 |
https://github.com/lukeroth/gdal 213 |
https://github.com/appelmar/gdalcubes 214 |
https://github.com/remotesensinginfo/rsgislib 215 |
https://github.com/jblindsay/whitebox-geospatial-analysis-tools 216 |
https://metacpan.org/pod/Geo::GDAL 217 |
https://github.com/PDAL/PDAL 218 |
https://github.com/davidfrantz/force 219 |
https://github.com/jdbcode/LLR-LandTrendr 220 |
https://github.com/Vizzuality/gfw 221 |
https://github.com/yannforget/conda-recipes 222 |
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https://github.com/senbox-org/s3tbx 229 |
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https://github.com/senbox-org/snap-engine 232 |
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https://github.com/m-mohr/stac-node-validator 237 |
https://github.com/vannizhang/aiforearth-landcover-app 238 |
https://github.com/emilyselwood/tiffhax 239 |
https://github.com/GERSL/Fmask 240 |
https://github.com/jjrom/resto 241 |
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https://github.com/EO-College/tomography_tutorial 244 |
https://github.com/profLewis/geog0111 245 |
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https://github.com/acgeospatial 247 |
https://github.com/acgeospatial/Satellite_Imagery_Python 248 |
https://github.com/acgeospatial/Geospatial_Python_CourseV1 249 |
https://github.com/patrickcgray/open-geo-tutorial 250 |
https://github.com/ceholden/open-geo-tutorial 251 |
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https://www.gis.usu.edu/~chrisg/python/2009/ 253 |
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https://github.com/techforspace/sentinel 255 |
https://www.tomasbeuzen.com/python-for-geospatial-analysis/README.html 256 |
https://www.futurelearn.com/courses/artificial-intelligence-for-earth-monitoring 257 |
https://github.com/chrieke/awesome-satellite-imagery-datasets 258 |
https://github.com/deepVector/geospatial-machine-learning 259 |
https://github.com/robmarkcole/satellite-image-deep-learning 260 |
https://github.com/ternaus/TernausNetV2 261 |
https://arxiv.org/abs/1806.00844 262 |
https://github.com/jensleitloff/CNN-Sentinel 263 |
https://github.com/Vooban/Smoothly-Blend-Image-Patches 264 |
https://forums.fast.ai/t/geospatial-deep-learning-resources-study-group/31044 265 |
https://github.com/meet-sapu/Crop-Yield-Prediction-Using-Satellite-Imagery 266 |
https://github.com/Lichtphyz/Houston_flooding 267 |
https://github.com/kkgadiraju/SatelliteImageClassification 268 |
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https://github.com/Paulymorphous/Road-Segmentation 270 |
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https://github.com/Geoyi/pixel-decoder 272 |
https://github.com/ucalyptus/Detecting-Ships 273 |
https://github.com/trailbehind/DeepOSM 274 |
https://github.com/MaxLenormand/Keras-for-computer-vision 275 |
https://medium.com/@kylepob61392/airplane-image-classification-using-a-keras-cnn-22be506fdb53 276 |
https://github.com/sshuair/torchsat 277 |
https://torchsat.readthedocs.io/en/latest/ 278 |
https://github.com/esowc/ml_drought 279 |
https://ml-clim.github.io/drought-prediction/ 280 |
https://github.com/gabrieltseng/pycrop-yield-prediction 281 |
https://datapink.io/datapink/neat-EO/ 282 |
https://github.com/lukasliebel/dfc2020_baseline 283 |
https://github.com/rhammell/planesnet 284 |
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https://github.com/rhammell/shipsnet-detector 286 |
https://github.com/shakasom/Deep-Learning-for-Satellite-Imagery 287 |
https://github.com/nshaud/DeepNetsForEO 288 |
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https://github.com/nealjean/predicting-poverty 301 |
https://github.com/darribas/satellite_led_liverpool 302 |
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https://github.com/marcbelmont/satellite-image-object-detection 304 |
https://github.com/descarteslabs/contrastive_sensor_fusion 305 |
https://github.com/ESA-PhiLab/ai4eo 306 |
https://github.com/ESA-PhiLab/NGVEO 307 |
https://github.com/ESA-PhiLab/iris 308 |
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https://github.com/adrianalbert/urban-environments 310 |
https://github.com/microsoft/AIforEarth-API-Development 311 |
https://github.com/microsoft/ai4eutils 312 |
https://gitlab.com/dai-projets/odeon-landcover 313 |
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https://github.com/perrygeo/docker-gdal-base 319 |
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https://www.youtube.com/watch?v=ZpA9jgSqAkk 340 |
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https://mygeoblog.com/2017/10/06/from-gee-to-numpy-to-geotiff/ 343 |
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https://github.com/dtarb/TauDEM 426 |
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https://elevationapi.com/ 428 |
https://github.com/cmla/s2p 429 |
https://github.com/mprakhar/DSM2DTM 430 |
https://ti.arc.nasa.gov/tech/asr/groups/intelligent-robotics/ngt/stereo/ 431 |
https://github.com/mortcanty/SARDocker 432 |
https://github.com/lveci/awesome-sar 433 |
https://github.com/johntruckenbrodt/pyroSAR 434 |
https://github.com/birgander2/PyRAT 435 |
https://github.com/dm6718/RITSAR 436 |
https://github.com/bminchew/PySAR 437 |
https://github.com/EO-College/sarbian 438 |
https://github.com/ESA-PhiLab/OpenSarToolkit 439 |
https://github.com/ESA-PhiLab/infrastructure 440 |
https://github.com/ESA-PhiLab/OST_Notebooks 441 |
https://github.com/johntruckenbrodt/S1_ARD 442 |
https://github.com/nansencenter/sea_ice_drift 443 |
https://github.com/benjimin/s1prepro 444 |
https://github.com/SpaceNetChallenge/SpaceNet_SAR_Buildings_Solutions 445 |
https://github.com/earthobservatory/sentinel1-opds 446 |
https://github.com/AndrewPham9/rice_sentinel1 447 |
https://github.com/nansencenter/sentinel1denoised 448 |
https://github.com/So-YeonBae/Sentinel1-Biodiversity 449 |
https://un-spider.org/advisory-support/recommended-practices/recommended-practice-flood-mapping/python-step-by-step 450 |
https://github.com/UN-SPIDER/radar-based-flood-mapping 451 |
https://github.com/stactools-packages/sentinel1 452 |
https://gist.github.com/bzgeo/950f3db986b3513311ed42efe2395171 453 |
https://github.com/icesat-2UT/PhoREAL 454 |
https://github.com/hobu/usgs-lidar 455 |
https://github.com/giswqs/lidar 456 |
https://github.com/EduinHSERNA/pyGEDI 457 |
https://gist.github.com/KMarkert/c68ccf53260d7b775b836bf2e11e2ec3 458 |
https://github.com/carlos-alberto-silva/rGEDI 459 |
https://github.com/remotesensinginfo/pysl4land 460 |
https://github.com/rodolfolotte/gedi 461 |
https://github.com/rbavery/sprnca_gedi 462 |
https://github.com/JohMast/GEDI_Yucatan 463 |
https://github.com/HeatherKmtb/q_research 464 |
https://github.com/isce-framework/isce3 465 |
https://github.com/yumorishita/LiCSBAS 466 |
https://github.com/insarlab/MintPy 467 |
https://pyrocko.org/ 468 |
https://github.com/levuvietphong/InSARFlow 469 |
https://github.com/GeoscienceAustralia/PyRate 470 |
https://github.com/aria-tools/ARIA-tools 471 |
https://github.com/EJFielding/ISCE_utils 472 |
https://github.com/RaphaelGrandin/ROI_PAC-Sentinel1 473 |
https://github.com/scottyhq/insar_scripts 474 |
https://github.com/bakerunavco/https://github.com/bakerunavco/pygmtsar 475 |
https://github.com/isce-framework/isce2 476 |
https://github.com/mdelgadoblasco/snap2stamps 477 |
https://github.com/JGCRI/demeter 478 |
https://github.com/openearth/videomap 479 |
https://github.com/nkeikon/GEDI-experiment 480 |
https://philippgaertner.github.io/ 481 |
https://labo.obs-mip.fr/multitemp/ 482 |
https://medium.com/the-downlinq 483 |
https://medium.com/@abt0020/extracting-canopy-height-with-gedi-data-5af8c87df158 484 |
https://github.com/esowc/challenges_2020 485 |
https://github.com/esowc/challenges_2021 486 |
https://github.com/jwagemann 487 |
https://spacenetchallenge.github.io/ 488 |
https://www.sentinel-hub.com/develop/community/contest/ 489 |
https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/overview 490 |
https://competitions.codalab.org/competitions/18544 491 |
https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection 492 |
https://www.crowdai.org/challenges/mapping-challenge 493 |
https://github.com/neptune-ai/open-solution-mapping-challenge 494 |
https://github.com/wekeo/AtmosHack2018 495 |
https://github.com/jncc/s1-s2-ard-code-list 496 |
https://twitter.com/pyGEDI 497 |
https://github.com/ceholden 498 |
https://github.com/chrieke 499 |
https://github.com/Fernerkundung/ 500 |
https://github.com/gena 501 |
https://github.com/jgomezdans 502 |
https://github.com/johntruckenbrodt 503 |
https://github.com/neteler 504 |
https://github.com/olivierhagolle 505 |
https://github.com/perrygeo 506 |
https://github.com/giswqs 507 |
https://github.com/rhammell 508 |
https://github.com/RemotePixel 509 |
https://github.com/robintw 510 |
https://github.com/rouault 511 |
https://github.com/samapriya 512 |
https://github.com/shakasom 513 |
https://github.com/yannforget 514 |
https://github.com/developmentseed 515 |
https://github.com/mapbox 516 |
https://github.com/planetlabs 517 |
https://github.com/DigitalGlobe 518 |
https://github.com/azavea 519 |
https://github.com/radiantearth 520 |
https://github.com/sentinel-hub 521 |
https://github.com/pytroll 522 |
https://github.com/CosmiQ 523 |
https://www.theia-land.fr/en/softwares-and-tools/ 524 |
https://github.com/sparkgeo 525 |
https://github.com/GeoscienceAustralia 526 |
https://github.com/dymaxionlabs 527 |
https://github.com/satellogic 528 |
https://github.com/senbox-org 529 |
https://github.com/nasa-gibs 530 |
https://github.com/mundialis 531 |
https://github.com/ESA-PhiLab 532 |
https://github.com/Element84 533 |
https://github.com/k-sys/covid-19 534 |
https://github.com/fchollet/deep-learning-with-python-notebooks 535 |
https://jakevdp.github.io/PythonDataScienceHandbook/ 536 |
https://www.freecodecamp.org/news/an-a-z-of-useful-python-tricks-b467524ee747/ 537 |
https://jalammar.github.io/visual-numpy/ 538 |
https://github.com/dunovank/jupyter-themes 539 |
https://github.com/mrgloom/awesome-semantic-segmentation 540 |
https://unidata.github.io/python-training/workshop/workshop-intro/ 541 |
https://github.com/ternaus/TernausNet 542 |
https://github.com/fangohr/introduction-to-python-for-computational-science-and-engineering 543 |
https://www.anotherbookondatascience.com/ 544 |
https://github.com/pydata/xarray 545 |
https://colab.research.google.com/github/ageron/handson-ml2/blob/master/tools_matplotlib.ipynb#scrollTo=gG7Fh4EMV2ey 546 |
https://towardsdatascience.com/springer-has-released-65-machine-learning-and-data-books-for-free-961f8181f189 547 |
https://www.youtube.com/watch?v=V0D2mhVt7NE 548 |
https://github.com/usmanr149/Classification-Algorithm 549 |
https://github.com/parrt/dtreeviz 550 |
https://github.com/gpetepg/python_tips 551 |
https://github.com/amueller/introduction_to_ml_with_python 552 |
https://nbviewer.jupyter.org/urls/gist.githubusercontent.com/Jwink3101/e6b57eba3beca4b05ec146d9e38fc839/raw/f486ca3dcad44c33fc4e7ddedc1f83b82c02b492/Matplotlib_Cheatsheet 553 |
https://github.com/GDSL-UL/Teaching_Links 554 |
https://dabeaz-course.github.io/practical-python/ 555 |
https://github.com/GeostatsGuy/Resources/blob/master/README.md 556 |
https://csgillespie.github.io/efficientR/ 557 |
https://github.com/csgillespie/efficientR 558 |
https://github.com/luigidifraia/jupyterhub-platform-tutorials 559 |
https://github.com/rockita/Environmental_Intelligence 560 |
https://earthdata.nasa.gov/eosdis/science-system-description/eosdis-components/gibs 561 |
https://github.com/Fernerkundung/awesome-sentinel 562 |
https://github.com/attibalazs/awesome-remote-sensing 563 |
https://github.com/sacridini/awesome-remote-sensing-papers 564 |
https://github.com/sacridini/Awesome-Geospatial 565 |
https://github.com/wenhwu/awesome-remote-sensing-change-detection 566 |
https://github.com/chrieke/awesome-geospatial-companies 567 |

568 | -------------------------------------------------------------------------------- /create-bookmarks-from-readme/create_bookmarks_html.py: -------------------------------------------------------------------------------- 1 | import requests 2 | from bs4 import BeautifulSoup, SoupStrainer 3 | from datetime import datetime 4 | today = str(datetime.today().strftime('%Y-%m-%d')) 5 | 6 | content = requests.get('https://github.com/acgeospatial/awesome-earthobservation-code/blob/master/README.md').content 7 | lslink =[] 8 | flag = False 9 | for link in BeautifulSoup(content, parse_only=SoupStrainer('a')): 10 | if hasattr(link, "href"): 11 | if (link['href']) == '#start-here': 12 | flag = True 13 | if (link['href']) == '#end': 14 | flag = False 15 | else: 16 | pass 17 | if flag == True: 18 | if 'https' in (link['href']): 19 | lslink.append(link['href']) 20 | else: 21 | pass 22 | 23 | ## adapted from https://pastebin.com/wf2SgCZp 24 | _OUTPUT_FILENAME = today+"-awesome-eo-code-bookmarks.html" 25 | 26 | with open(_OUTPUT_FILENAME, 'w') as f: 27 | 28 | # Write the header 29 | f.write(""" 30 | 33 | 34 | Awesome-EO-code-Bookmarks 35 |

EO-Bookmarks

36 |

\n""") 37 | 38 | for row in lslink: 39 | f.write('\t

') 42 | f.write(row) 43 | f.write('\n') 44 | 45 | f.write('

\n') 46 | -------------------------------------------------------------------------------- /create-bookmarks-from-readme/readme.md: -------------------------------------------------------------------------------- 1 | This folder contains some super hacky python code that will read the contents of 2 | https://github.com/acgeospatial/awesome-earthobservation-code/blob/master/README.md 3 | 4 | and extract all the links (between '#start-here' and '#end' to a .html file that you can load into your browser) 5 | 6 | See the example.html file as example output 7 | -------------------------------------------------------------------------------- /presentations/20200501_Awesome-EarthObservation-Code_Barsc_Cuddle.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/acgeospatial/awesome-earthobservation-code/6756673ecf44fcc247619ef8aa082b2dcddc57e2/presentations/20200501_Awesome-EarthObservation-Code_Barsc_Cuddle.pdf -------------------------------------------------------------------------------- /presentations/20200617_FOSS4GUK_Awesome-EarthObservation-Code.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/acgeospatial/awesome-earthobservation-code/6756673ecf44fcc247619ef8aa082b2dcddc57e2/presentations/20200617_FOSS4GUK_Awesome-EarthObservation-Code.pdf -------------------------------------------------------------------------------- /presentations/readme.md: -------------------------------------------------------------------------------- 1 | This folder will contain pdfs of any presentations given related to awesome-earthobservation-code 2 | --------------------------------------------------------------------------------