├── 2017-reinvent.md ├── 2018-ps-summit.md ├── 2018-reinvent.md ├── 2019-ps-summit.md ├── 2019-reinvent.md ├── 2020-reinvent.md └── README.md /2017-reinvent.md: -------------------------------------------------------------------------------- 1 | ## [AWS re:Invent 2017](https://reinvent.awsevents.com/) Geospatial Talks 2 | Talks, sessions and workshops that may be of interest to those working with geospatial data. PRs accepted! 3 | 4 | ### [STG205 - #EarthonAWS: How NASA Is Using AWS](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=14954) 5 | 6 | [Video](https://www.youtube.com/watch?v=Sh7FB-tkYXM) | [Slides](https://www.slideshare.net/AmazonWebServices/stg205earthonaws-how-nasa-is-using-aws) 7 | 8 | Organizations around the world are facing a “data tsunami” as next-generation sensors produce enormous volumes of earth observation data. Come learn how NASA is leveraging AWS to efficiently work with data and computing resources at a massive scale. NASA is transforming its earth science EOSDIS (Earth Observing System Data Information System) program by moving data processing and archiving to the cloud. NASA anticipates that their data archives will grow from 16 PB today to over 400 PB by 2023 and 1 Exabyte by 2030. They’re moving to the cloud to scale their operations for this new paradigm. 9 | 10 | ### [ABD402 - How Esri Optimizes Massive Image Archives for Analytics in the Cloud](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=14666) 11 | 12 | [Video](https://www.youtube.com/watch?v=U486YxlDoeM) | [Slides](https://www.slideshare.net/AmazonWebServices/how-esri-optimizes-massive-image-archives-for-analytics-in-the-cloud-abd402-reinvent-2017) 13 | 14 | Petabyte scale archives of satellites, planes, and drones imagery continue to grow exponentially. They mostly exist as semi-structured data, but they are only valuable when accessed and processed by a wide range of products for both visualization and analysis. This session provides an overview of how ArcGIS indexes and structures data so that any part of it can be quickly accessed, processed, and analyzed by reading only the minimum amount of data needed for the task. In this session, we share best practices for structuring and compressing massive datasets in Amazon S3, so it can be analyzed efficiently. We also review a number of different image formats, including GeoTIFF (used for the Public Datasets on AWS program, Landsat on AWS), cloud optimized GeoTIFF, MRF, and CRF as well as different compression approaches to show the effect on processing performance. Finally, we provide examples of how this technology has been used to help image processing and analysis for the response to Hurricane Harvey. 15 | 16 | ### [EUT302 - Data Ingestion at Seismic Scale: Best practices for processing petabyte scale HPC workloads in the Cloud](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=14599) 17 | 18 | [Video](https://www.youtube.com/watch?v=wq4mgC9FCRA) | [Slides](https://www.slideshare.net/AmazonWebServices/eut302data-ingestion-at-seismic-scale-best-practices-for-processing-petabyte-scale-hpc-workloads-in-the-cloud) 19 | 20 | With geoseismic datasets that are petabytes in size and growing, finding tomorrow's energy is increasingly data and compute intensive. Hess Corporation, a global energy company, needed to be able to respond quickly to changing oil market demands, while minimizing costs. By migrating petabytes of data and running high performance computing (HPC) workloads on AWS, Hess reduced compute costs and accelerated time in which geologists received results. In this session, you will learn how Hess built a GeoSeismic data repository on AWS, by leveraging S3 and EFS, and processes that data by building HPC clusters on-demand using the GPU-enabled P2 instance family. Additionally, you will learn how the Hess subsurface computing team was able to move from running on premise cap-ex driven GPU clusters to an op-ex driven on-demand model in the AWS cloud. 21 | 22 | ### [CMP201 - Auto Scaling: The Fleet Management Solution for Planet Earth](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=14605) 23 | 24 | [Video](https://www.youtube.com/watch?v=WUUbOQyrnJU) | [Slides](https://www.slideshare.net/AmazonWebServices/auto-scaling-the-fleet-management-solution-for-planet-earth-cmp201-reinvent-2017) 25 | 26 | Auto Scaling allows cloud resources to scale automatically in reaction to the dynamic needs of customers. This session shows how Auto Scaling offers an advantage to everyone—whether it's basic fleet management to keep instances healthy as an Amazon EC2 best practice, or dynamic scaling to manage extremes. We share examples of how Auto Scaling helps customers of all sizes and industries unlock use cases and value. We also discuss how Auto Scaling is evolving to scaling different types of elastic AWS resources beyond EC2 instances. Data Scientist & Principal Investigator, Hook Hua, from NASA Jet Propulsion Laboratory (JPL) / California Institute of Technology will share how Auto Scaling is used to scale science data processing of remote sensing data from earth-observing satellite missions, and reduce response times during hazard response events such as those from earthquakes, hurricanes, floods, and volcanoes. JPL will also discuss how they are integrating their science data systems with the AWS ecosystem to expand into NASA’s next two large-scale missions with remote-sensing radar-based observations. Learn how Auto Scaling is being used at a global scale – and beyond! 27 | 28 | ### [CON326 - Remote Sensing and Image Processing on AWS](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=16433) 29 | 30 | Video | Slides 31 | 32 | Learn how Encirca services by DuPont Pioneer utilizes Amazon ECS powered by GPU-instances and EC2 Spot instances to run proprietary image processing algorithms against satellite imagery. Mark Lanning and Ethan Harstad, engineers at DuPont Pioneer will show how this architecture has allowed them to process satellite imagery multiple times a day for each agricultural field in the United States in order to identify crop health changes. 33 | 34 | #### [ARC326 - Create a Serverless Image Processing Platform](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=16122) 35 | 36 | Video | [Slides](https://www.slideshare.net/AmazonWebServices/create-a-serverless-image-processing-platform-arc326-reinvent-2017) 37 | 38 | Are you interested in processing images at scale without launching a single virtual machine? In this workshop, we show participants how to create an entirely serverless image processing platform using Amazon Cognito, AWS Lambda, Amazon Rekognition, and Amazon Elasticsearch Service (Amazon ES). Participants leave this workshop with a web portal where users can upload images that ultimately end up in a searchable index powered by Amazon ES and Kibana. 39 | 40 | ### [CMP213 - GPU (G3) Applications in Media and Entertainment Workloads](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=16798) 41 | 42 | [Video](https://www.youtube.com/watch?v=eMKzJnMLS3U) | [Slides](https://www.slideshare.net/AmazonWebServices/cmp213gpug3-applications-in-media-and-entertainment-workloads) 43 | 44 | GPUs have a large application in Media and Entertainment workloads. From backend video processing and creation workloads such as VFX/Rendering, transcoding and broadcast playout to high-end creatives as well as video editing workloads. Backed by the NVIDIA Tesla M60 GPUs, G3 instances offer unparalleled power and flexibility to do complex modeling, 3D visualization, computer aided design, seismic visualization, video encoding. G3 instances are the first Amazon EC2 instances to support NVIDIA GRID Virtual Workstation capabilities, with streaming support for four monitors each with up to 4K resolution, and hardware encoding to support up to 10 High Efficiency Video Coding (HEVC) H.265 1080p30 streams or up to 18 H.264 1080p30 streams per GPU for faster video frame processing and improved image fidelity. In this session we will highlight two criticial Media workloads Video Editing via remote application streaming and Broadcast Playout origination from the AWS cloud. We will have Pop Media discuss their remote video editing in the cloud that enables secure remote, real-time editorial and image processing session views. This will be followed by Evertz regarding Discovery Channel’s broadcast Playout application for several live Discovery channels currently. 45 | 46 | ### [CMP216 - Use Amazon EC2 Spot Instances to Deploy a Deep Learning Framework on Amazon ECS](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=16676) 47 | 48 | Video | [Slides](https://www.slideshare.net/AmazonWebServices/cmp216use-amazon-ec2-spot-instances-to-deploy-a-deep-learning-framework-on-amazon-ecs) 49 | 50 | Deep learning, an implementation of machine learning, uses neural networks to solve complex problems like computer vision, natural language processing, and recommendations. Deep learning libraries and frameworks enable developers to enhance the capabilities of their applications and projects. In this workshop, learn how to build and deploy a powerful deep learning framework, Apache MXNet, on containers. The portability and resource management benefit of containers enables developers to focus less on infrastructure and more on building. The lab first demonstrates the automation capabilities of AWS CloudFormation to stand up core infrastructure. We also leverage Spot Fleet for the cost benefit of using Spot Instances, especially important for developer environments. Next we create an MXNet container in Docker and deploy it with Amazon ECS. Finally, we explore image classification with MXNet to validate that everything is working as expected. 51 | 52 | ### [MCL405 - Custom Image Recognition with Deep Learning Using Apache MXNet and Convolutional Neural Networks](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=14645) 53 | 54 | Video | Slides 55 | 56 | Convolutional Neural Networks (CNNs) are a category of deep learning algorithms. They were first brought to prominence in 2012 when Alex Krizhevsky used them to win that year’s ImageNet competition to find the best computer vision algorithms. Since then, they have emerged as one of the most influential innovations in computer vision, and have been used to identify objects like faces and traffic signs, in addition to powering vision in robots and self-driving cars. In this Chalk Talk, we explain how CNNs work and demonstrate how to train an image recognition model on your custom image repositories using Gluon, the new intuitive, dynamic programming interface for Apache MXNet. 57 | 58 | ### [SRV315 - How We Built a Mission-Critical, Serverless File Processing Pipeline for over 100 Million Photos](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=14823) 59 | 60 | [Video](https://www.youtube.com/watch?v=Hy2n-fC-r98) | [Slides](https://www.slideshare.net/AmazonWebServices/srv331build-a-multiregion-serverless-application-for-resilience-and-high-availabilitypdf) | [Slides](https://www.slideshare.net/AmazonWebServices/srv315how-we-built-a-missioncritical-serverless-file-processing-pipeline-for-over-100-million-photos) 61 | 62 | In this session, principal architect Mike Broadway describes how HomeAway built a high-throughput, scalable pipeline for manipulating, storing, and serving hundreds of image files every second with Lambda, Amazon S3, DynamoDB, and Amazon SNS. He also shares best practices and lessons learned as they scaled their mission-critical On Demand Image Service (ODIS) system into production. Lambda functions form the backbone of ODIS, which handles over 100 million photographs that are uploaded to HomeAway's vacation rental platform. HomeAway is a vacation rental marketplace with more than 2 million rentals in 190 countries and is part of Expedia. 63 | 64 | ### [SRV318 - Research at PNNL: Powered by AWS](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=15015) 65 | 66 | [Video](https://www.youtube.com/watch?v=2-1T-ly1ylE) | [Slides](https://www.slideshare.net/AmazonWebServices/srv318research-at-pnnl-powered-by-aws) 67 | 68 | Pacific Northwest National Laboratory's rich data sciences capability has produced novel solutions in numerous research areas including image analysis, statistical modeling, and social media (and many more!). See how PNNL software engineers utilize AWS to enable better collaboration between researchers and engineers, and to power the data processing systems required to facilitate this work, with a focus on Lambda, EC2, S3, Apache Nifi and other technologies. Several approaches will be covered including lessons learned. 69 | 70 | ### [SRV333 - Designing and Implementing a Serverless Media Processing Workflow Using AWS Step Functions](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=15438) 71 | 72 | Video | [Slides](https://www.slideshare.net/AmazonWebServices/designing-and-implementing-a-serverless-media-processing-workflow-using-aws-step-functions-srv333-reinvent-2017) 73 | 74 | This workshop demonstrates how to use AWS Step Functions to coordinate multiple AWS Lambda functions using visual workflows. You learn how to build a Step Functions state machine to orchestrate a multi-step serverless application. You work in teams to design and implement an image recognition and processing workflow using AWS Step Functions, AWS Lambda, Amazon S3, Amazon DynamoDB, and Amazon Rekognition. The workflow process photos uploaded to Amazon S3 and extract metadata from the image, such as geolocation, size, format, and time. It then uses image recognition to tag objects in the photo and produce a thumbnail. Prerequisites: Experience using AWS, an AWS account, AWS CLI. We provide AWS credits for use in the hands-on lab. Bring a laptop. 75 | 76 | ### [WPS204 - Effective Emergency Response in AWS](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=15017) 77 | 78 | [Video](https://www.youtube.com/watch?v=Ud73Sq1zzkQ) | [Slides](https://www.slideshare.net/AmazonWebServices/wps204effective-emergency-response-in-awspdf) 79 | 80 | Emergencies happen with no notice, whether with weather-related events or man-made incidents. Countless lives can be saved not only by predicting disastrous events but also by reacting quickly and effectively. In this session, you will discover how organizations are using AWS capabilities to predict and respond to emergencies around the world. StormSense, a project led by City of Virginia Beach enhances the capability of VA Beach and the neighboring communities of Hampton Roads, VA to predict coastal flooding resulting from storm surge, rain, and tides in ways that are replicable, scalable, measurable, and make a difference worldwide. LiveSafe, A communications platform to improve safety and prevention efforts - puts a mobile security system in the hands of everyone in organization, deputizing employees so they can feel involved and empowered to do something when they see something. LiveSafe’s cloud-based command dashboard receives tips in real time and allows security officials to respond via secure live chat. 81 | -------------------------------------------------------------------------------- /2018-ps-summit.md: -------------------------------------------------------------------------------- 1 | ## [AWS Public Sector Summit 2018](https://aws.amazon.com/summits/public-sector-summit-washington-dc-2018/) Geospatial Talks 2 | Talks, sessions and workshops that may be of interest to those working with geospatial data. PRs accepted! 3 | 4 | ### How Element 84 Raises the Bar on Streaming Satellite Data 5 | 6 | [Video](https://www.youtube.com/watch?v=zEhwCMIyG7Q&t=0s&list=PLhr1KZpdzukeEdgc5W4So6x1rufYyjzNW&index=97) | [Slides](https://www.slideshare.net/AmazonWebServices/how-element-84-raises-the-bar-on-streaming-satellite-data) 7 | 8 | GOES-16 is a source of critical data for monitoring smoke, flooding impacts, burn scars, volcanic ash, and weather. However, finding and using this data can require significant investment. Element 84 married video compression and streaming technology with NASA’s Cumulus data processing pipeline, plus AWS Managed Services, to make the entire GOES-16 archive interactive on an array of formats. Users can now easily identify dates of interest for events like natural disasters, and stage a subset of the archive for analysis. And all of this scales down to $0 when not in use. 9 | 10 | ### Machine Learning with Earth Observation Imagery 11 | 12 | [Video](https://www.youtube.com/watch?v=G3IT8TZ4tP8&t=0s&list=PLhr1KZpdzukeEdgc5W4So6x1rufYyjzNW&index=96) | [Slides](https://www.slideshare.net/AmazonWebServices/machine-learning-with-earth-observation-imagery?qid=4b2cc900-2f7d-46d9-9694-8dd108f44cf2&v=&b=&from_search=1) 13 | 14 | For just a moment, think of the immense amount of data generated by Earth-observing systems. The sheer volume often makes it impractical for humans alone to perform the analysis, and accordingly, many groups are turning to artificial intelligence (AI) and machine learning (ML) algorithms to support their analysis. We'll hear from Development Seed and EOS about how they are using AI and ML to unlock the power of this planetary-scale data that is becoming increasingly more accessible in the cloud. From open-source libraries and human-in-the loop initial processing passes, to fully automated pipelines, we'll examine the new capacity for analysis made possible by technology. 15 | 16 | ### Making Sense of Remote Sensing 17 | 18 | [Video](https://www.youtube.com/watch?v=fsMoLRZyamA) | [Slides](https://www.slideshare.net/AmazonWebServices/making-sense-of-remote-sensing?qid=dcd07eab-b7df-43d5-98c8-1999cac97be3&v=&b=&from_search=1) 19 | 20 | SkyWatch is all about making Earth-observation data digestible and accessible. They believe that creating a single place to bring together the planet’s observational datasets will make new waves in geospatial analytics. In this session, we'll take a look at how companies can take advantage of cloud-native workflows to enable access and analysis across planetary-scale datasets. You’ll hear how SkyWatch leveraged AWS serverless technologies to build a company that transforms petabytes of sensor data from space into useful information. You'll also learn how Sinergise is merging a variety of data streams through products like Sentinel Hub, and creating actionable intelligence for its users. 21 | 22 | ### AWS Public Datasets: Learnings from Staging Petabytes of Data for Analysis in AWS 23 | 24 | [Video](https://www.youtube.com/watch?v=n72atn00-AI) | [Slides](https://www.slideshare.net/AmazonWebServices/aws-public-datasets-learnings-from-staging-petabytes-of-data-for-analysis-in-aws?qid=c3f16a89-bd01-46b6-ad74-a0e7fc6b9304&v=&b=&from_search=2) 25 | 26 | AWS hosts a variety of public data sets that anyone can access for free. Previously, large datasets such as satellite imagery or genomic data have required hours or days to locate, download, customize, and analyze. When data is made publicly available on AWS, anyone can analyze any volume of data without needing to download or store it themselves. The AWS Open Data Team will share tips and tricks, patterns and anti-patterns and tools to help you most effectively stage your data for analysis in the cloud. 27 | 28 | Thursday, Jun 21, 2:00 PM - 2:50 PM – Room 144 ABC 29 | 30 | ### Global Open Transportation Projects 31 | 32 | The need for public and private entities to collaborate and exchange data about transportation-related issues is becoming more and more prominent. This session will feature The World Bank describing DRIVER, a web and mobile application enabling field data collection and analysis of road safety incidents, currently being tested and piloted in several countries, primarily in Asia. Also featured is SharedStreets, a non-profit developing a global data infrastructure for streets, and creating new models for third-party data exchange. 33 | 34 | Thursday, Jun 21, 9:00 AM - 9:50 AM – Room 144 ABC 35 | 36 | ### Using AWS and Open Data to Meet the Demands of Disaster Response Situations 37 | 38 | [Video](https://www.youtube.com/watch?v=ZyW1am1CVFM&t=0s&list=PLhr1KZpdzukeEdgc5W4So6x1rufYyjzNW&index=80) | [Slides](https://www.slideshare.net/AmazonWebServices/using-aws-and-open-data-to-meet-the-demands-of-disaster-response-situations?qid=8385f193-8c23-46df-bc35-bf7db3d3ccbd&v=&b=&from_search=1) 39 | 40 | The cloud can enable groups to support humanitarian and disaster response partners through all phases of the response cycle. We'll hear from the Humanitarian OpenStreetMap Team about how they are using AWS along with open-source tools to deploy a fully scalable system that can meet the needs of emergency activations and crisis response situations. We'll also take a look at a prototype for monitoring and starting a response chain based on the USGS Earthquake Notification Service. 41 | 42 | Thursday, Jun 21, 11:00 AM - 11:50 AM – Room 144 ABC 43 | 44 | ### AWS Disaster Response: Using Deployable Native AWS Services for your Rapid Response Infrastructure 45 | 46 | [Video](https://www.youtube.com/watch?v=_gttyWnVWjs&t=0s&list=PLhr1KZpdzukeEdgc5W4So6x1rufYyjzNW&index=16) | [Slides](https://www.slideshare.net/AmazonWebServices/aws-disaster-response-using-deployable-native-aws-services-for-your-rapid-response-infrastructure?qid=8385f193-8c23-46df-bc35-bf7db3d3ccbd&v=&b=&from_search=2) 47 | 48 | During and after a disaster, the speed, timeliness, and accuracy of response and recovery efforts can be the difference that saves many lives. In this session, you will be introduced to the Amazon Web Services Disaster Response Program (AWS DRP). We will provide an overview of the program, its purpose, services, and the capabilities designed to assist customers responding to disasters and/or emergencies. This session will also provide insights into “the Art of the Possible” as it relates to how the various native AWS Services can be used to deliver the capabilities required to assist customers in accessing critical data, infrastructure, and decision making tools during disasters. 49 | 50 | Thursday, Jun 21, 3:05 PM - 3:55 PM – Room 152 AB 51 | 52 | ### Transitioning Geoscience Research to the Cloud: Opportunities and Challenges 53 | 54 | [Video](https://www.youtube.com/watch?v=D6x9aj4jF5U&t=0s&list=PLhr1KZpdzukeEdgc5W4So6x1rufYyjzNW&index=81) | [Slides](https://www.slideshare.net/AmazonWebServices/transitioning-geoscience-research-to-the-cloud-opportunities-and-challenges?qid=c144ec02-bcbd-4160-9404-1f1b7316053f&v=&b=&from_search=1) 55 | 56 | NASA, Alaska Satellite Facility and the University of Washington are all going through the process of empowering groups of internal and external users to work with planetary scale datasets. This session will look at the various techniques used to power those internal revolutions and lower the cost of knowledge for all. 57 | 58 | Thursday, Jun 21, 10:00 AM - 10:50 AM – Room 144 ABC 59 | 60 | ### Real-Time Machine Learning on Satellite Imagery: How DigitalGlobe Uses Amazon SageMaker to Massively Scale-up Information Extraction from Satellite Imagery 61 | 62 | [Video](https://www.youtube.com/watch?v=E_nUM_ufKCA&t=0s&list=PLhr1KZpdzukeEdgc5W4So6x1rufYyjzNW&index=62) | [Slides](https://www.slideshare.net/AmazonWebServices/altime-machine-learning-on-satellite-imagery-how-digitalglobe-uses-amazon-sagemaker-to-massively-scaleup-information-extraction-from-satellite-imagery?qid=73ff6839-f566-4112-9ecb-8ce129b338f0&v=&b=&from_search=1) 63 | 64 | If you have ever searched for directions, called an Uber, or looked up a trailhead, you have used DigitalGlobe’s imagery or information derived from it. DigitalGlobe went all-in on AWS to meet the growing demand for commercial geo-intelligence, migrating its entire 18-year imagery archive to the cloud. With the new WorldView Legion satellite constellation coming online, DigitalGlobe looked to leverage AWS for migrating their GBDX machine learning software to autonomously analyze incoming data at scale. GBDX leverages Amazon SageMaker to drastically speed-up both inferencing & training of machine learning models, ushering in a new era of rapid feedback & iteration: a data scientist’s dream come true. 65 | 66 | Thursday, Jun 21, 9:00 AM - 9:50 AM – Room 150 AB 67 | 68 | ### Machine Learning, Open Data, and the Future WarFighter 69 | 70 | [Video](https://www.youtube.com/watch?v=SwAmFEtsQy8&t=0s&list=PLhr1KZpdzukeEdgc5W4So6x1rufYyjzNW&index=85) | [Slides](https://www.slideshare.net/AmazonWebServices/machine-learning-open-data-and-the-future-warfighter?qid=69950d77-a408-48ba-80f3-f7747f92a823&v=&b=&from_search=1) 71 | 72 | This session highlights how Earth observation data shared in the cloud is accelerating research in machine learning that can have a dramatic impact on the effectiveness of future warfighting capability. Come learn about SpaceNet, a project sponsored by CosmiQ Works, DigitalGlobe, and Nvidia that makes commercial satellite imagery available for machine learning research on AWS. In this session, you will learn how AWS machine learning services like SageMaker, hyper-scale GPU compute capacity, and datasets shared in the cloud can ultimately produce machine learning models that could have a dramatic impact on the effectiveness of future warfighting capability. As the DoD strives to bring innovation directly to the warfighter, a combination of global open data sets coupled with easily built ML models can give warfighters access to critical information when the mission needs it most. 73 | 74 | Wednesday, Jun 20, 9:00 AM - 9:50 AM – Room 146 B 75 | 76 | 77 | 78 | -------------------------------------------------------------------------------- /2018-reinvent.md: -------------------------------------------------------------------------------- 1 | ## [AWS re:Invent 2018](https://reinvent.awsevents.com/) Geospatial Talks 2 | Talks, sessions and workshops that may be of interest to those working with geospatial data. PRs accepted! 3 | 4 | ### [AIM334 - Build Models for Aerial Images Using Amazon SageMaker](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=88725) 5 | 6 | [Video](#) | [Slides](https://www.slideshare.net/AmazonWebServices/build-models-for-aerial-images-using-amazon-sagemaker-aim334-aws-reinvent-2018) 7 | 8 | There are unique challenges to building highly accurate models that detect small objects in aerial and overhead imagery. In this chalk talk, we dive deep into using convolutional neural networks (CNNs) with Amazon SageMaker in order to build and train aerial object detection models. We build advanced models using AWS public datasets, such as SpaceNet and LandSat, as we work with DigitalGlobe's GBDX Notebooks. 9 | 10 | 11 | ### [WPS302 - Best Practices for Working with Large-Scale Geospatial Imagery](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=90201) 12 | 13 | [Video](#) | [Slides](#) 14 | 15 | Across the commercial and public sectors, companies are working with large geospatial datasets. In this session, we review how to use various services, including Amazon S3, Amazon Glacier, Amazon Athena, AWS Batch, and AWS Step Functions to store, process, and get insights into your large geospatial datasets. 16 | 17 | 18 | ### [AIM428 - Building, Training, and Deploying fast.ai Models Using Amazon SageMaker](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=88751) 19 | 20 | [Video](#) | [Slides](https://www.slideshare.net/AmazonWebServices/building-training-and-deploying-fastai-models-using-amazon-sagemaker-aim428-aws-reinvent-2018) 21 | 22 | In a short space of time, fast.ai has become a popular Deep Learning library, driven by the success of the fast.ai online Massive Open Online Course (MOOC). It has allowed SW developers to achieve, in the span of a few weeks, state-of-the-art results in domains such as Computer Vision (CV), Natural Language Processing (NLP), and structured data machine learning. In this chalk talk, we go into the details of building, training, and deploying fast.ai-based models using Amazon SageMaker. 23 | 24 | 25 | ### [WPS326 - AWS Public Data Sets: How to Stage Petabytes of Data for Analysis in AWS](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=90146) 26 | 27 | [Video](#) | [Slides](https://www.slideshare.net/AmazonWebServices/aws-public-data-sets-how-to-stage-petabytes-of-data-for-analysis-in-aws-wps326-aws-reinvent-2018) 28 | 29 | AWS hosts a variety of public data sets that anyone can access for free. Previously, large data sets such as satellite imagery or genomic data have required hours or days to locate, download, customize, and analyze. When data is made publicly available on AWS, anyone can analyze any volume of data without downloading or storing it themselves. In this session, the AWS Open Data Team shares tips and tricks, patterns and anti-patterns, and tools to help you effectively stage your data for analysis in the cloud. 30 | 31 | 32 | ### [WPS315 - National Geospatial-Intelligence Agency: Changing the Way the Intelligence Community Moves Data](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=90139) 33 | 34 | [Video](https://www.youtube.com/watch?v=KXelfBpJtDY) | [Slides](https://www.slideshare.net/AmazonWebServices/national-geospatialintelligence-agency-changing-the-way-the-intelligence-community-moves-data-wps315-aws-reinvent-2018) 35 | 36 | In this session, we feature the U.S. National Geospatial-Intelligence Agency (NGA), a key stakeholder and sponsor for the new AWS Secret Region, which supports workloads up to the Secret U.S. security classification level and is readily available to the U.S. Intelligence Community (IC). NGA uses AWS Snowball Edge to support War Fighter, utilizing imagery from NGA’s Open Data Store and implementing geospatial applications on the edge. AWS Snowball Edge allows NGA to directly support its mission, providing products and services to decision makers, warfighters, and first responders when they need it most. Enabling the edge changes NGA’s ability to share critical resources, data to facilitate user access meets NGA’s mission needs, and support the IC and Department of Defense as a whole. 37 | 38 | 39 | ### [WPS324 - Building Your Geospatial Data Lake](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=90145) 40 | 41 | [Video](#) | [Slides](https://www.slideshare.net/AmazonWebServices/building-your-geospatial-data-lake-wps324-aws-reinvent-2018) 42 | 43 | In this chalk talk, we discuss how to design a data lake, and how to permission different groups and applications to access and analyze datasets. Learn from subject-matter experts about a variety of AWS technologies for populating your data lake, monitoring new ingestion, and processing data for meaningful analysis. We examine considerations for structured data, such as relevant database engines with geospatial support, as well as considerations for unstructured data in the form of object storage. In addition, we address how to protect and secure data based on an organization’s needs. 44 | 45 | ### [CMP340 - How Jupiter Intel Uses AWS to Forecast Weather Impact & Climate Risk](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=89709) 46 | 47 | [Video](#) | [Slides](https://www.slideshare.net/AmazonWebServices/how-jupiter-intel-uses-aws-to-forecast-weather-impact-climate-risk-cmp340-aws-reinvent-2018) 48 | 49 | Jupiter is a cloud-native company that delivers hyperlocal environmental information in a changing climate, primarily using AWS Batch. Through AWS Batch’s capability to execute thousands of scientific modeling jobs while managing scale and cost, Jupiter scientists can focus on data analysis and developing sophisticated machine learning (ML)-based applications to support private sector and local municipality customers; AWS Batch takes care of the rest. In this chalk talk, we demonstrate how AWS Batch, through managing resource provisioning and scheduling, enables flexibility across changing requirements to allow various modeling applications to run quickly and at scale. 50 | 51 | ### [DEM22 - Data Quakes: Seismic Analysis with Athena and Amazon Redshift](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=89250) 52 | 53 | [Video](https://www.youtube.com/watch?v=5c5Lgy0TTy0) | [Slides](#) 54 | 55 | What’s the shakeup in Silicon Valley? Join us as we investigate global subduction zones, highlighting and plotting areas with the deepest earthquakes. Using Amazon Athena, Amazon Redshift, and Matillion ETL for Amazon Redshift, we prepare a semistructured geospatial dataset from the International Federation of Digital Seismograph Networks for visualization. Learn how to build a best-practice architecture using Athena to read and flatten Amazon S3 data, Matillion ETL to perform the more complex data enrichment, and Amazon Redshift for aggregation, before handing off the data to Amazon QuickSight for visualization. This presentation is brought to you by AWS partner, Matillion Limited. 56 | 57 | ### [AIM323 - Build a Searchable Image Library with Amazon Rekognition](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=89919) 58 | 59 | [Video](#) | [Slides](#) 60 | 61 | Join us for a deep dive on building a searchable image library using Amazon Rekognition. We walk though creating a search index for objects and scenes so you can quickly retrieve images using labels created from automatic metadata extraction. Also learn how to use AWS Lambda to automatically maintain your image library. 62 | 63 | ### [ARC329 - Massively Parallel Data Processing at Scale](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=88753) 64 | 65 | [Video](#) | [Slides](#) 66 | 67 | Get hands on with serverless data processing at scale. In this session, we use Landsat 8 satellite imagery to calculate a Normalized Difference Vegetation Index (NDVI) across multiple points of interest in the world using the GeoTIFF data across multiple spectral bands. 68 | 69 | ### [CMP416 - Build High-Performance, Cloud-Native, Open-Source Apps on AWS & Save](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=91429) 70 | 71 | [Video](#) | [Slides](#) 72 | 73 | As a leader in agriculture technologies and services, Bayer is using technologies such as unmanned aerial vehicles (UAV), satellite imagery, and sensor data from multiple sources to generate real time insights. Over 300 data sources are ingested into their open source HPC geospatial platform to generate on average 100M API calls per day. The platform is used to provide real-time visualization and computational analysis to Bayer’s internal research community, partners, and is licensed to third-party applications to provide insights relevant to high-yield production of crops. In this session, Mendez-Costabel discusses how Bayer transitioned from on-premises packaged software architecture to open-source software and cloud services from AWS to build a modern, scalable, high-performance, open-source app on AWS. Learn about the open-source application architecture and AWS services used. Learn how the computing environment has changed the way that Bayer is performing R&D projects, and how the move to a modern architecture has enabled Bayer’s customers to gain insights that are transforming their businesses. 74 | 75 | ### [LFS303 - AI/ML in Life Sciences: Predictive Modeling with Amazon SageMaker](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=89077) 76 | 77 | [Video](#) | [Slides](https://www.slideshare.net/AmazonWebServices/aiml-in-life-sciences-predictive-modeling-with-amazon-sagemaker-lfs303-aws-reinvent-2018) 78 | 79 | Learn how to use Amazon SageMaker and other AI/ML services on AWS to build predictive data models for life sciences. In this workshop, you’ll learn how to incorporate diverse data types ranging from sensor data to imagery, into machine learning models. At the end of this workshop, you’ll be able to deploy these models to monitor real-world scenarios in life sciences. 80 | 81 | ### [CMP326 - Use HPC on AWS for Physics-Based Simulation, ML, and Statistics in CAE](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=89693) 82 | 83 | [Video](#) | [Slides](https://www.slideshare.net/AmazonWebServices/use-hpc-on-aws-for-physicsbased-simulation-ml-and-statistics-in-cae-cmp326-aws-reinvent-2018) 84 | 85 | In this session, learn how an AWS HPC customer in the aerospace engineering segment migrated key parts of their computer-aided engineering (CAE) simulation and visualization applications to AWS to improve infrastructure redundancy for a robust process and user experience, achieving a system speed-up of 18X. 86 | 87 | ### [ANT358 - Serverless Stream Processing Tips & Tricks](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=90087) 88 | 89 | [Video](#) | [Slides](https://www.slideshare.net/AmazonWebServices/serverless-stream-processing-tips-tricks-ant358-aws-reinvent-2018) 90 | 91 | Streaming data ingestion and near real-time analysis gives you immediate insights into your data. By using AWS Lambda with Amazon Kinesis, you can obtain these insights without the need to manage servers. But are you doing this in the most optimal way? In this interactive session, we review the best practices for using Lambda with Kinesis, and how to avoid common pitfalls. 92 | 93 | ### [CMP336 - Optimize Amazon EC2 Instance, AWS Fargate Container, & Lambda Function](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=89708) 94 | 95 | [Video](#) | [Slides](https://www.slideshare.net/AmazonWebServices/optimize-amazon-ec2-instances-aws-fargate-containers-lambda-functions-cmp336-aws-reinvent-2018) 96 | 97 | AWS offers a wide selection of compute platforms. In this session, we highlight key platform features of different Amazon EC2 instance families, and provide a framework in which to choose the best compute resource (including Amazon EC2 Instance, AWS Fargate Container, and AWS Lambda function) for your workloads based on metrics and workload profiles. We also share best practices and performance tips for getting the most out of your Amazon EC2 instances to help you reduce unnecessary spending and improve application performance. 98 | 99 | 100 | ### [ANT208 - Serverless Video Ingestion & Analytics with Amazon Kinesis Video Streams](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=22937) 101 | 102 | [Video](https://www.youtube.com/watch?v=mrLsGq0HFVk) | [Slides](https://www.slideshare.net/AmazonWebServices/serverless-video-ingestion-analytics-with-amazon-kinesis-video-streams-ant208-aws-reinvent-2018) 103 | 104 | Amazon Kinesis Video Streams makes it easy to capture live video, play it back, and store it for real-time and batch-oriented ML-driven analytics. In this session, we first dive deep on the top five best practices for getting started and scaling with Amazon Kinesis Video Streams. Next, we demonstrate a streaming video from a standard USB camera connected to a laptop, and we perform a live playback on a standard browser within minutes. We also have on stage members of Amazon Go, who are building the next generation of physical retail store experiences powered by their "just walk out" technology. They walk through the technical details of their integration with Kinesis Video Streams and highlight their successes and difficulties along the way. 105 | -------------------------------------------------------------------------------- /2019-ps-summit.md: -------------------------------------------------------------------------------- 1 | ## [AWS Public Sector Summit 2019](https://aws.amazon.com/summits/washington-dc/) Geospatial Talks 2 | Talks, sessions and workshops that may be of interest to those working with geospatial data. PRs accepted! 3 | 4 | ### [294628 - Introduction to AWS Ground Station](https://awsdc19.smarteventscloud.com/connect/sessionDetail.ww?SESSION_ID=294628) 5 | 6 | [Video](https://www.youtube.com/watch?v=YhZe6eOPoRQ) 7 | 8 | AWS Ground Station is a fully managed service that lets you control satellite communications, downlink and process satellite data, and scale your satellite operations quickly, easily and cost-effectively without having to worry about building or managing your own ground station infrastructure. In this session, we introduce AWS Ground Station and how it works, starting from understanding the ground station console and onboarding process. We demonstrate how to schedule a contact, gather data, and build architectures to extract data from AWS Ground Station for analysis and storage with other AWS services. 9 | 10 | ### [294833 - From Australia to Africa - A Digital Earth Journey](https://awsdc19.smarteventscloud.com/connect/sessionDetail.ww?SESSION_ID=294833) 11 | 12 | [Video](https://www.youtube.com/watch?v=zOhMwE9VvME) 13 | 14 | Digital Earth Africa (DE Africa) will enable African nations to track changes across their countries and the continent in unprecedented detail. Based largely on openly available satellite data and leveraging technology and services developed in collaboration with Geoscience Australia and the Committee on Earth Observation Satellites (CEOS), DE Africa will provide insights on a wide range of issues, including floods, drought, soils, coastal erosion, agriculture, forests, land use, water availability and quality, as well as changes to human settlements. DE Africa is supported by the Group on Earth Observations (GEO), a partnership of more than 100 member countries and 130 partners that envision a future where decisions and actions for the benefit of humankind are informed by coordinated, comprehensive and sustained Earth observations. This session will cover how the initiative fits within the GEO Work Programme, successes from related projects and how the platform operates on AWS. 15 | 16 | ### [294880 - Leveraging Earth Observations and Cloud Technology for Global Sustainable Development](https://awsdc19.smarteventscloud.com/connect/sessionDetail.ww?SESSION_ID=294880) 17 | 18 | [Video](https://www.youtube.com/watch?v=6f2Qf_ZEJ7o) 19 | 20 | Global sustainable development seeks to promote prosperity while protecting the planet. Earth observations play an important role in monitoring targets, tracking progress, and helping nations and stakeholders make informed decisions. However, this data is not always easily accessible and users may not have the compute power necessary to take advantage of these resources through their own on-premises data centers. The recently launched Amazon Sustainability Data Initiative significantly reduces the cost, time, and technical barriers associated with analyzing large datasets to generate sustainability insights. In this session, we will hear from the Radiant.Earth Foundation, the Group on Earth Observation and Digital Earth Africa on ways Earth observations and AWS cloud technology are supporting governments, NGOs, businesses, and individuals to make more informed decisions and manage challenges such as climate change, soil and coastal erosion, deforestation, desertification, and water scarcity. 21 | 22 | ### [295421 - Getting Started with Serverless Architectures](https://awsdc19.smarteventscloud.com/connect/sessionDetail.ww?SESSION_ID=295421) 23 | 24 | [Video](https://www.youtube.com/watch?v=UhIuK5ty9Ww) 25 | 26 | Join this session to learn about serverless architectures, their benefits, and the basics of the AWS Serverless stack (e.g., AWS Lambda, Amazon API Gateway, and AWS Step Functions). We will discuss how to use serverless architectures for a variety of use cases. Learn practical tips, tricks, and take-home architecture patterns to implement immediately. 27 | 28 | ### [295440 - Improving Information and Communications in a Disaster Scenario with AWS Snowball Edge](https://awsdc19.smarteventscloud.com/connect/sessionDetail.ww?SESSION_ID=295440) 29 | 30 | [Video](https://www.youtube.com/watch?v=E_xHnfsvl2w) 31 | 32 | Volunteers and emergency personnel carefully coordinate their response to natural disasters. This coordination requires data and making data actionable and accessible at the tactical edge remains a challenge. We'll dive into the results of our disaster response user needs study and invite Element 84 on stage to exhibit their prototype disaster response pipeline for field data management. The serverless, cloud-based pipeline combines public and private data sources with open source software. It can provide the field with a ruggedized remote data center (SBE), preloaded with critical information, including reach-back capabilities. You'll see how this works firsthand and learn ways first responders can update data from in-situ sources such as drones. 33 | 34 | ### [295498 - Build Highly Accurate Training Datasets Using Amazon SageMaker Ground Truth](https://awsdc19.smarteventscloud.com/connect/sessionDetail.ww?SESSION_ID=295498) 35 | 36 | [Video](https://www.youtube.com/watch?v=6DcbMwfQclY) 37 | 38 | Amazon SageMaker Ground Truth helps you build highly accurate training datasets for machine learning quickly. Not only does Amazon SageMaker Ground Truth offer easy access to public and private human labelers, but it also provides labelers with built-in workflows and interfaces for common labeling tasks. Join us to understand how this new service works, see a demonstration of Ground Truth in action, and find out ways to use Amazon SageMaker Ground Truth to lower your labeling costs. 39 | 40 | ### [295887 - Aircraft to Clean Energy: How Government and Regulated Industries are Transforming Product Lifecycle Management with AWS GovCloud (US)](https://awsdc19.smarteventscloud.com/connect/sessionDetail.ww?SESSION_ID=295887) 41 | 42 | [Video](https://www.youtube.com/watch?v=RVyrfmTMztM) 43 | 44 | Product Lifecycle Management (PLM) solutions are the record of authority for mission critical product development and sustainment programs, intellectual property, program management, and business processes. Deploying PLM solutions for government and highly regulated industries using traditional on premise approaches has proven costly, time consuming and inflexible. In this session, Collins Aerospace and Smartronix will discuss how they have transformed internal and end customer enterprise PLM strategies with industry solutions from Dassault Systemes and Siemens PLM with AWS GovCloud (US). Learn how many organizations in this space have leveraged the compliance features of AWS to manage sensitive data associated with PLM workloads, and realize the business value and agility offered by cloud. Speakers will discuss technical evaluation and analysis of alternatives, solution implementation, data security and compliance as well as business outcomes. 45 | 46 | ### [296694 - Innovating Humanitarian & Disaster Response with AI/ML](https://awsdc19.smarteventscloud.com/connect/sessionDetail.ww?SESSION_ID=296694) 47 | 48 | [Video](https://www.youtube.com/watch?v=tA1pDPJSrTs) 49 | 50 | Understand how nonprofits, non-governmental agencies, and governments are using AI/ML to predict disasters before they happen, and take action earlier to save lives. Harnessing existing weather forecast data, food price data agricultural production data, satellite imagery and other data pipelines, and mobilizing responses faster is transforming how disaster response can be coordinated. Learn how Amazon SageMaker models are identifying patterns early, along with AWS Snowball, AWS Snowball Edge, and image analysis. 51 | 52 | ### [299940 - Networking Patterns and Practices: A Case Study of NASA Goddard Space Flight Center’s Cloud Journey](https://awsdc19.smarteventscloud.com/connect/sessionDetail.ww?SESSION_ID=299940) 53 | 54 | [Video](https://www.youtube.com/watch?v=LWK83O5sGCg) 55 | 56 | Learn common networking patterns for connecting to AWS resources securely and reliably through the prism of NASA Goddard Space Flight Center's journey with AWS. We'll discuss lessons learned and solutions developed from implementing cloud in support of the Space Mission at GSFC. Explore use-case examples of AWS Networking innovations including Resource sharing, PrivateLink, AWS Transit Gateway, and AWS DirectConnect Gateway. We'll also explore performance considerations and the new client AWS VPN service. 57 | 58 | ### [301009 - Increasing the Use and Value of Earth Science Data and Information](https://awsdc19.smarteventscloud.com/connect/sessionDetail.ww?SESSION_ID=301009) 59 | 60 | [Video](https://www.youtube.com/watch?v=xpdte04B2Ww) 61 | 62 | The Earth Science Information Partners (ESIP) is a nonprofit member organization supported by NASA, NOAA, and USGS, with a belief that the quality of life, economic opportunities, and stewardship of the planet are enhanced by regular use of timely, scientifically sound Earth science data. Many agencies, academic institutions, and scientists rarely have the resources and expertise to explore computationally intensive Earth science research in the cloud. Still, the opportunity is within reach. Through small grant funding, AWS Cloud research credits, and community input, ESIP’s lab supports projects aimed at adopting community-accepted best practices in scientific data management and analysis. In this session we highlight several lab projects, including how one team is leveraging the Pangeo project on AWS to build an open-source pipeline to infer snow cover at meter-scale resolution from CubeSat data, using machine learning. 63 | 64 | ### [301075 - Accelerating Time to Science Using Cloud](https://awsdc19.smarteventscloud.com/connect/sessionDetail.ww?SESSION_ID=301075) 65 | 66 | [Video](https://www.youtube.com/watch?v=5Pt-3BK0kJc) 67 | 68 | Can cloud power ground-breaking research? This session provides a deeper understanding of ways cloud computing can accelerate scientific discoveries. The National Science Foundation is changing research communities in fields ranging from biology to astronomy to education, through its Harnessing the Data Revolution. In South America, the government of Chile dives deep into its Data Observatory initiative, which is expected to create a single digital platform involving a combination of astronomy, data science, informatics, and communication technologies. And in the United States, the Cornell Lab demonstrates how it uses eBird to study migration habits, species movement, population density, light pollution, and climate change. Join us to witness firsthand how cloud computing is revolutionizing the future of research, and see for yourself what this future really looks like. 69 | 70 | ### [301095 - Seeing the Stars Through the Cloud](https://awsdc19.smarteventscloud.com/connect/sessionDetail.ww?SESSION_ID=301095) 71 | 72 | [Video](https://www.youtube.com/watch?v=Bek6TOlQpM0) 73 | 74 | New astronomy projects are emerging amid the troves of data new telescopes produce. This session explores the government of Chile's cloud transformation to create a digital platform that provides an interdisciplinary field of study involving astronomy, data science, informatics, and information/communications technologies. These data, including information about light emitted from some of the coldest objects, help astrophysicists explore the universe's greatest secrets. We go beyond examples found in astronomy and provide insights that can be applied across other industry verticals. We also examine the use of Amazon S3, Amazon SageMaker, EC2, Amazon FSx for Lustre, Lambda, DynamoDB, API Gateway, and Amazon Sumerian. 75 | 76 | ### [301582 - Community Tools for Analysis of Earth Science Data in the Cloud](https://awsdc19.smarteventscloud.com/connect/sessionDetail.ww?SESSION_ID=301582) 77 | 78 | [Video](https://www.youtube.com/watch?v=VD1yHAhykVE) 79 | 80 | The Pangeo project is building a flexible, open framework for scientific analysis and visualization based on tools widely used in the Python ecosystem. We will demonstrate the flexibility of the system, showing how the user needs only their browser to access the framework via JupyterHub. Data is stored in cloud-friendly formats on Amazon S3, while Xarray provides tools for efficient data manipulation. Dask schedules fine-grained parallelization on a Kubernetes cluster, and PyViz provides interactive visualization. We will also discuss performance, security, and transferability across public cloud platforms, costs to operate, and approaches to encourage a cultural shift in scientific computation. 81 | 82 | ### [302789 - SpaceNet: Accelerating Machine Learning for Foundational Mapping Challenges](https://awsdc19.smarteventscloud.com/connect/sessionDetail.ww?SESSION_ID=302789) 83 | 84 | [Video](https://www.youtube.com/watch?v=GmhmeoVoooA) 85 | 86 | SpaceNet is a nonprofit LLC designed to accelerate machine learning against geospatial problems, such as mapping road network routes after a natural disaster using exclusively remote sensing data. Over the last two and half years, SpaceNet has released over 6500 sq km of high-resolution satellite imagery, with ~800,000 building footprint labels and 8000 sq km of road network labels. In addition to open sourcing a large, curated data set, SpaceNet has developed and administered four data science challenges to solve the problem of extracting building footprint and road networks from satellite imagery at scale. We will discuss the challenges of deploying these machine learning algorithms in operational timelines, and how AWS products be used to accelerate delivery of timely information derived from satellite imagery after a natural disaster. We will also highlight upcoming analytic challenges. 87 | 88 | ### [316604 - Building a Serverless GIS / Geo Positioning and Alerting Solution](https://awsdc19.smarteventscloud.com/connect/sessionDetail.ww?SESSION_ID=316604) 89 | 90 | [Video](#) 91 | 92 | Traditional commercial Geo monitoring GPS solutions need servers running around the clock, require expensive mapping licenses, and only work with proprietary GPS devices that lack compatibility. In this talk we will explore how to build a 100% serverless GIS/Geo monitoring GPS system, using open data to visualize the geographic information without GIS licenses fees. Our solution has a built-in alerting system based on Amazon Connect and will be open to any device (IoT, Mobile, GPS device) to provide a geofencing capability. This solution is based on a serverless paradigm, freeing customers to focus on applications on top of the solution. Join us to discover how it's done! 93 | 94 | ### [317954 - From Unattended Ground Sensors (UGS) to Installations; Leveraging AWS IoT for Effective, Flexible Sensing](https://awsdc19.smarteventscloud.com/connect/sessionDetail.ww?SESSION_ID=317954) 95 | 96 | [Video](https://www.youtube.com/watch?v=M6XjgALs7f4) 97 | 98 | Attendees will learn how to apply cutting edge AWS IoT and machine learning technologies at the edge. The session will cover a broad set of use cases from the remote disconnected fringe, to situational awareness for an installation. Novetta will demonstrate a flexible sensor platform, which uses business logic and machine learning to interpret sensor outputs for a broad array of physical security and tactical missions. This session is sponsored by Novetta. 99 | 100 | ### [320996 - Deep Dive on S3 Glacier Deep Archive](https://awsdc19.smarteventscloud.com/connect/sessionDetail.ww?SESSION_ID=320996) 101 | 102 | [Video](https://www.youtube.com/watch?v=HDbl4MZQhtw) 103 | 104 | Amazon S3 Glacier Deep Archive is a new storage class that provides secure, durable object storage for long-term data retention and digital preservation. S3 Glacier Deep Archive is designed for customers that retain data sets for 7-10 years or longer to meet business or regulatory compliance requirements, such as organizations in media and entertainment, financial services, healthcare, and public sectors. At just $0.00099 per GB-month (less than one-tenth of one cent, or $1 per TB-month), S3 Glacier Deep Archive offers the lowest cost storage class in the cloud, at prices significantly less expensive than storing and maintaining data in on-premises magnetic tape libraries and/or archiving data offsite. 105 | 106 | ### [324287 - Speeding Up Scientific Computation in Astrophysics with Amazon Web Services](https://awsdc19.smarteventscloud.com/connect/sessionDetail.ww?SESSION_ID=324287) 107 | 108 | [Video](https://www.youtube.com/watch?v=PCaAbfFjP7o) 109 | 110 | Astrophysics has just entered a new era where computational power (parallel and distributed) and state-of-the-art technology are mandatory as off the-shelf tools for daily activity. In this talk we focus on two scenarios in which AWS services (Amazon EC2, Amazon SQS, AWS Lambda, and Amazon S3) have greatly improved project performance. In the first, we explore the use of EC2 coupled with AWS Lambda in heavily parallelized CUDA based simulations to design innovative instrumentation in search for life on extrasolar planets. Next, we dive into serverless HTC architecture used to create simulations of astrophysical sources such as astronomical high energy gamma rays. You'll leave understanding how these architectures propelled innovation and decreased execution times all while optimizing overall cost. 111 | -------------------------------------------------------------------------------- /2019-reinvent.md: -------------------------------------------------------------------------------- 1 | ## [AWS re:Invent 2019](https://reinvent.awsevents.com/) Geospatial Talks 2 | Talks, sessions and workshops that may be of interest to those working with geospatial data. PRs accepted! 3 | 4 | ### [WPS319-R - Best practices for working with large-scale geospatial data ](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=96944&csrftkn=1DVK-VLXR-QY5Z-UCLL-BHAT-VQ4F-UUIN-DH7X) 5 | 6 | Video | Slides 7 | 8 | Across the commercial and public sectors, companies are working with large geospatial datasets. We take a look at how to use various services including Amazon Simple Storage Service (Amazon S3), Amazon Simple Storage Service Glacier, Amazon Athena, AWS Step Functions, and AWS Batch to store, process, and get insights into your large geospatial datasets. Please bring your laptop. 9 | 10 | ### [AIM366-R - SpaceNet: ML to solve mapping challenges](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=98587&csrftkn=1DVK-VLXR-QY5Z-UCLL-BHAT-VQ4F-UUIN-DH7X) 11 | 12 | Video | Slides 13 | 14 | SpaceNet, a nonprofit LLC focusing on solving geospatial problems such as mapping road network routes after a natural disaster, has open-sourced more than 6,500 square kilometers of high-resolution satellite imagery with approximately 800,000 building footprint labels and 8,000 square kilometers of road network labels. Join us for a discussion on how to use this data to train machine learning algorithms that help disseminate timely information in the aftermath of natural disasters. 15 | 16 | ### [WPS305 - Building your geospatial data lake](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=95440&csrftkn=1DVK-VLXR-QY5Z-UCLL-BHAT-VQ4F-UUIN-DH7X) 17 | 18 | Video | Slides 19 | 20 | In this session, learn how to design a data lake and how to give permission to different groups and applications to access and analyze datasets. Hear from subject-matter experts about a variety of AWS technology for populating your data lake, monitoring new ingestion, and processing data for meaningful analysis. We also examine considerations for structured data, such as relevant database engines with geospatial support, as well as considerations for unstructured data in the form of object storage. Finally, learn how to protect and secure data based on your organization’s needs. 21 | 22 | ### [CON330 - Running Kubernetes clusters at scale: Bird](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=98800&csrftkn=1DVK-VLXR-QY5Z-UCLL-BHAT-VQ4F-UUIN-DH7X) 23 | 24 | [Video](https://youtu.be/WVbyQvPa5O8) | Slides 25 | 26 | Launched in fall 2017, Bird is a micromobility company that enables access to shared e-scooters and lightweight electric vehicles in 100+ locations worldwide. Join us to hear how building a modern stack on top of Amazon EKS has enabled Bird to quickly ramp up its development in order to provide business value in a stable and secure manner. Further, learn how Bird’s backend utilizes native AWS services like Amazon S3 and Amazon SQS, open-source streaming systems like Kafka and Flink, and a modern microservices architecture to turn terabytes of geospatial data into the mobility revolution of the future. 27 | 28 | ### [STG401 - Manage objects and optimize cost with Amazon S3 and Amazon S3 Glacier](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=95639&csrftkn=1DVK-VLXR-QY5Z-UCLL-BHAT-VQ4F-UUIN-DH7X) 29 | 30 | Video | Slides 31 | 32 | In this workshop, get hands-on experience managing large amounts of data in Amazon S3. Learn best practices for configuring and managing object policies, including storage buckets, security, regulatory compliance, data replication, and more. We review optimizing storage costs, data lifecycle management, and retrieval times through the various restore tiers and SLAs, and we share strategies for moving and organizing data in formats that take advantage of Amazon S3 features. We also discuss how efficient Amazon S3 clients use the Amazon S3 API. In addition, learn about listing objects, Amazon S3 inventory, Amazon S3 batch operations, and Amazon S3 Select. 33 | 34 | ### [WPS306 - AWS Public Datasets: Lessons from staging petabytes of data for analysis ](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=95441&csrftkn=1DVK-VLXR-QY5Z-UCLL-BHAT-VQ4F-UUIN-DH7X) 35 | 36 | Video | Slides 37 | 38 | AWS hosts a variety of public datasets that anyone can access for free. Previously, large datasets such as satellite imagery or genomic data have required hours or days to locate, download, customize, and analyze. By making data available publicly on AWS, anyone can analyze any volume of data without needing to download or store it themselves. In this session, the AWS Open Data team shares tips and tricks, patterns and anti-patterns, and tools to help you most effectively stage your data for analysis in the cloud. 39 | 40 | ### [HAC201 - Code Green: Hacking on Amazon open sustainability data](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=99788&csrftkn=1DVK-VLXR-QY5Z-UCLL-BHAT-VQ4F-UUIN-DH7X) 41 | 42 | Video | Slides 43 | 44 | Code Green invites you to enter a hackathon involving Amazon Sustainability Data Initiative (ASDI) datasets. Individual - or groups of two to five - hackers work on projects during re:Invent, with demos presented Thursday. Any ASDI dataset can be used. In particular, check out weather and climate. APIs, visualization tools, combinations of datasets into something new - all fair game. Entries should be open-source GitHub repos. Want to participate without competing? An ASDI workshop runs concurrently during the event Thursday. Code Green is purchasing carbon offsets for travel to re:Invent by all registered hackathon participants; hackathon winners receive free passes to re:Invent 2020. The real winner? Earth, of course. 45 | 46 | ### [WPS323 - Monitoring the Earth without costing the world](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=97783&csrftkn=1DVK-VLXR-QY5Z-UCLL-BHAT-VQ4F-UUIN-DH7X) 47 | 48 | [Video](https://youtu.be/sVMXR2qCE5E) | Slides 49 | 50 | The BlueDot Water Observatory is an Earth-observation-based solution that provides reliable and timely information about surface water levels across the globe. Cost-effective yet reliable solutions for monitoring water resources are needed, as ground-based monitoring networks are often too costly and, in some cases, also unreliable. Sinergise shows how using global satellite imagery available on Amazon Simple Storage Service (Amazon S3) through the AWS Public Dataset Program, combined with an efficient use of services including AWS Lambda, Amazon DynamoDB, and Amazon CloudWatch, you can carry out a global-scale project cheaper than previously possible. 51 | 52 | ### [STG352 - Edge computing in disaster response with Snowball Edge](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=97718&csrftkn=1DVK-VLXR-QY5Z-UCLL-BHAT-VQ4F-UUIN-DH7X) 53 | 54 | Video | Slides 55 | 56 | Emergency personnel have to carefully coordinate their response to natural disasters across many teams. This coordination requires data, especially actionable mapping data. However, getting access to data at the tactical edge is challenging. In this session, we demonstrate an architecture and pipeline for managing data for field scenarios with the ruggedized AWS Snowball Edge. The serverless, cloud-based pipeline combines public and private data sources with open-source software that can be preloaded on Snowball Edge. See how it works firsthand, and ask questions to learn how you could put such edge computing to work in your field scenarios—even with drones. 57 | 58 | ### [STG353 - Edge computing, IoT & machine learning in disaster response](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=97722&csrftkn=1DVK-VLXR-QY5Z-UCLL-BHAT-VQ4F-UUIN-DH7X) 59 | 60 | Video | Slides 61 | 62 | Many government agencies and commercial organizations come together to respond to natural disaster emergencies in the field. Such responders need computing resources at the tactical edge for communications, data gathering, and reconnaissance, often in dangerous or unstable environments. In this session, we discuss a solution architecture combining AWS Snowball Edge, IoT sensors, and C4ISR software that was deployed as the AWS Disaster Response Action team tested the tracking of search parties and recovery vehicles. We also demonstrate how machine learning at the edge can augment traditional sensor data to better support first responders and decision makers during disaster scenarios. 63 | 64 | ### [NET409-R - Processing AWS Ground Station data in AWS](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=97368&csrftkn=1DVK-VLXR-QY5Z-UCLL-BHAT-VQ4F-UUIN-DH7X) 65 | 66 | Video | Slides 67 | 68 | In this session, build a processing chain in AWS using Amazon Elastic Compute Cloud (Amazon EC2), Amazon Virtual Private Cloud (Amazon VPC), and Amazon Simple Storage Service (Amazon S3) to take weather satellite imagery data from the AWS Ground Station service and process it to finished images in Amazon S3. Learn about the different streaming data formats that AWS Ground Station provides and the steps involved in processing that data through different layers and components such as reliable delivery, demodulation and decoding, data recovery, and developing an output product. Please bring your laptop. 69 | 70 | ### [AIM320-R - Amazon SageMaker Ground Truth: Create a data labeling workflow](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=99052&csrftkn=1DVK-VLXR-QY5Z-UCLL-BHAT-VQ4F-UUIN-DH7X) 71 | 72 | Video | Slides 73 | 74 | Amazon SageMaker Ground Truth helps you build and manage highly accurate training datasets quickly. Ground Truth offers easy access to public and private human labelers and provides them with prebuilt workflows and interfaces for common labeling tasks. During this builders session, we show you how to start and use a workflow. Please bring your laptop. 75 | 76 | ### [NET401-R - Build your AWS Ground Station mission profile with AWS CloudFormation ](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=95869&csrftkn=1DVK-VLXR-QY5Z-UCLL-BHAT-VQ4F-UUIN-DH7X) 77 | 78 | Video | Slides 79 | 80 | Join us for a technical deep dive that provides you training to build a mission profile using AWS CloudFormation. In this session, you learn the satellite onboarding process for AWS Ground Station, build a mission profile that properly configures the antenna system before the pass, and directs your data flows between the antenna system and your VPC. You even get to watch a satellite contact! Please bring your laptop. 81 | 82 | ### [AIM308 - Build accurate training datasets with Amazon SageMaker Ground Truth](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=95394&csrftkn=1DVK-VLXR-QY5Z-UCLL-BHAT-VQ4F-UUIN-DH7X) 83 | 84 | [Video](https://youtu.be/6WJxzKsIFKA) | Slides 85 | 86 | Successful machine learning models are built on high-quality training datasets. Typically, the task of data labeling is distributed across a large number of humans, adding significant overhead and cost. This session explains how Amazon SageMaker Ground Truth reduces cost and complexity using techniques designed to improve labeling accuracy and reduce human effort. We walk through best practices for building highly accurate training datasets and discuss how you can use Amazon SageMaker Ground Truth to implement them. 87 | 88 | ### [NET308-R - Enabling automated astrophysics with AWS Ground Station](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=95859&csrftkn=1DVK-VLXR-QY5Z-UCLL-BHAT-VQ4F-UUIN-DH7X) 89 | 90 | Video | Slides 91 | 92 | NASA Jet Propulsion Laboratory’s (JPL) IT Chief Technology Officer, Tom Soderstrom, presents a demonstration of automated scheduling with AWS Ground Station and a NASA JPL satellite. The satellite, ASTERIA, used for this demonstration was designed in collaboration between the Massachusetts Institute of Technology and NASA JPL. AWS Ground Station connects antenna systems to cloud technologies so that researchers and scientists can automate their projects in space. 93 | 94 | ### [WPS309 - Aiding natural-disaster responses with Amazon SageMaker and Amazon Comprehend](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=95471&csrftkn=1DVK-VLXR-QY5Z-UCLL-BHAT-VQ4F-UUIN-DH7X) 95 | 96 | Video | Slides 97 | 98 | The first hour after a natural disaster is often referred to as the “golden hour," when responders have the highest chance of saving the lives of those effected by the disaster—but it's also when they have least amount of information available to them. In this chalk talk, we walk through different ways machine learning can help accelerate a responder’s understanding of the areas impacted and their ability to begin formulate potential recovery strategies. We also discuss how we leveraged Amazon SageMaker, In-Q-Tel’s SpaceNet dataset, Amazon Comprehend, AWS Lambda, Amazon API Gateway, and much more to build the prototype solution. 99 | 100 | ### [SVS340-R - Serverless image processing workflows at scale with AWS Step Functions](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=96592&csrftkn=1DVK-VLXR-QY5Z-UCLL-BHAT-VQ4F-UUIN-DH7X) 101 | 102 | Video | Slides 103 | 104 | With AWS Lambda, you can write code to process images and files without provisioning servers. But at scale, how do you coordinate multiple parallel processing steps, catch errors and retry failures, and keep your code modular and maintainable? AWS Step Functions comes to the rescue. In this workshop, you design and implement a distributed state machine to orchestrate a multi-step image recognition and processing workflow using Amazon Rekognition and AWS Step Functions. 105 | 106 | ### [AIM301-R - Creating high-quality training datasets with data labeling](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=95395&csrftkn=1DVK-VLXR-QY5Z-UCLL-BHAT-VQ4F-UUIN-DH7X) 107 | 108 | Video | Slides 109 | 110 | Amazon SageMaker Ground Truth makes it easy to quickly label high-quality, accurate training datasets. In this workshop, we set up labeling jobs for text and images to help you understand how to make the most of Amazon SageMaker Ground Truth. You learn how to explore and prepare the dataset and label it with object bounding boxes. Then, we use Amazon SageMaker to train a Single Shot MultiBox Detector (SSD) object-detection model based on the labeled dataset, use hyperparameter optimization to find the best model for deployment, and deploy the model to an endpoint for use in an application. 111 | 112 | ### [ANT222-R - Analytics with Amazon Athena](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=97956&csrftkn=1DVK-VLXR-QY5Z-UCLL-BHAT-VQ4F-UUIN-DH7X) 113 | 114 | Video | Slides 115 | 116 | Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon Simple Storage Service (Amazon S3) using standard SQL. Athena is serverless, and customers pay only for the queries they run. In this workshop, we dive deep into various use cases for Athena, including building applications that run on schedules, using CTAS and INSERT INTO as effective self-service ETL tools, and querying storage formats. We also look at authorization, authentication, and managing costs. If you are a data engineer looking to onboard your organization to Athena, this workshop equips you with the tools to be successful. 117 | 118 | ### [AIM227-S - Powering global-scale predictive intelligence using HPC on AWS ](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=98730&csrftkn=1DVK-VLXR-QY5Z-UCLL-BHAT-VQ4F-UUIN-DH7X) 119 | 120 | [Video](https://youtu.be/c3HIPExo3HI) | Slides 121 | 122 | Learn how Maxar and Descartes Labs run complex, global-scale models on Amazon EC2 instances powered by Intel Xeon Scalable processors. Maxar discusses its experience setting up an operational HPC cluster to run global numerical weather prediction models, obtaining performance that eclipses the speed of the NOAA bare-metal supercomputer. Descartes Labs shows how its platform enables hyper-scale object detection on satellite imagery accelerated by Intel AVX-512 instructions. It also shares its experience deploying tightly coupled HPC applications that use spot blocks at many-thousand processor scale, using HPC clusters built on AWS instances and powered by Intel Xeon Scalable processors. This presentation is brought to you by Intel, an APN Partner. 123 | 124 | ### [AIM329 - Using deep learning to track wildfires and air quality](https://www.portal.reinvent.awsevents.com/connect/sessionDetail.ww?SESSION_ID=98495&csrftkn=1DVK-VLXR-QY5Z-UCLL-BHAT-VQ4F-UUIN-DH7X) 125 | 126 | Video | Slides 127 | 128 | ALERTWildfire is a camera-based network infrastructure that captures satellite imagery of wildfires. In this chalk talk, we discuss deep-learning techniques that use this satellite imagery along with meteorological data to track wildfires and predict air quality in real time. 129 | -------------------------------------------------------------------------------- /2020-reinvent.md: -------------------------------------------------------------------------------- 1 | ## [AWS re:Invent 2020](https://reinvent.awsevents.com/) Geospatial Talks 2 | Talks, sessions and workshops that may be of interest to those working with geospatial data. PRs accepted! 3 | 4 | ### Using open data for sustainable agriculture 5 | 6 | [Details](https://virtual.awsevents.com/media/1_639lwpt1) 7 | 8 | As the world population expands and food insecurity reaches record levels, the collective need for agriculture to produce more output with fewer resources is critical. Using Earth observation data brings insights to agriculture and helps inform improved practices and outcomes in farms from Africa to Brazil and beyond. In this session, hear how you can find publicly available Earth observation data relevant for agriculture on AWS, and learn how customers like OneSoil, Sinergise, and Digital Earth Africa are using the cloud to build tools that enable renewable practices. 9 | 10 | 11 | ### Detecting extreme weather events from space 12 | 13 | [Details](https://virtual.awsevents.com/media/1_oyohaqwe) 14 | 15 | Satellites revolving around the earth collect petabytes of data every day and send them back for ingestion, categorization, processing, and dissemination. While more organizations dedicate resources to environmental monitoring and predictive analytics, it is essential to not just give them the platform to implement their technologies but also share the responsibility in solving the problem. In this session, learn how to use AWS Ground Station, Amazon SageMaker, and data lakes to automate, scale extreme weather event detection, and help build disaster resilience. You also hear how Fireball International delivers early wildfire detection services through multi-sensor rapid wildfire intelligence. 16 | 17 | 18 | ### Advancing the future of space in the cloud 19 | 20 | [Details](https://virtual.awsevents.com/media/1_qt6uzh27) 21 | 22 | Aerospace and intelligence companies are going all in on AWS to automate and scale space operations. Take a deep dive into how Maxar Technologies is using AWS to advance the future of space in the cloud using AWS Ground Station, AWS storage solutions, machine learning, and high-performance computing to predict where clouds and storms will be in order to deliver actionable earth intelligence to the world. Learn how Maxar uses the AWS Cloud and how they design space infrastructure to accelerate space exploration to the moon, Mars, and beyond. 23 | 24 | 25 | ### Building resilient cities with AWS IoT and data services 26 | 27 | [Details](https://virtual.awsevents.com/media/1_plh76ddd) 28 | 29 | In response to COVID-19, city planners more than ever need situational awareness of what is happening in their cities. This session brings together cross-domain skills in IoT, connectivity, and data analytics. Learn how to integrate IoT networks (such as LoRaWAN) with AWS Lake Formation data lakes and present geospatial analytics using Amazon QuickSight. Additionally, you learn how this reference architecture can be extended with AWS Marketplace solutions. Finally, the session includes a demonstration incorporating simulated city data. 30 | 31 | 32 | ### Fighting wildfire with artificial intelligence 33 | 34 | [Details](https://virtual.awsevents.com/media/1_frvuaybl) 35 | 36 | Fueled by heat and wind, wildfires are burning throughout states on the West Coast of the United States. As part of its continued efforts to reduce the risk of wildfire, San Diego Gas & Electric (SDG&E) is building machine learning models using AWS services to automatically identify asset damage on drone imagery and vegetation risks on satellite imagery. Also, to improve customer awareness ahead of public safety power shutoff events, the company has expanded its communication channels to smart assistants that enable convenient access to important information. 37 | 38 | 39 | ### Automating wind farm maintenance using drones and AI 40 | 41 | [Details](https://virtual.awsevents.com/media/1_depxopvz) 42 | 43 | We can use the power of drones, machine learning, and Internet of Things on the edge and the cloud to make turbine maintenance safer and more cost-effective. In this scenario, drones take pictures of turbines, while the solution analyzes the photos to detect damage or issues on the structure, achieving safer, quicker, and more accurate inspections. The inspection outputs are then used in business intelligence for issue monitoring, analytics, and forecasting for better decision-making processes. The project also showcases how the capabilities of digital twin technology can be leveraged for remote monitoring of wind farms. 44 | 45 | 46 | ### Drones and Snowballs: Delivering imagery at the edge 47 | 48 | [Details](https://virtual.awsevents.com/media/1_8i3sjqcu) 49 | 50 | After disasters and in rural areas, connectivity is limited, intermittent, or not available. As drone usage increases for disasters and humanitarian use cases, the ability to process imagery in no- or low-connectivity environments gives organizations information critical to planning and response. Take a deep dive to learn how the AWS Disaster Response program collaborated with NGOs like Help.NGO to prepare, provision, and operate a drone imagery pipeline at the edge using AWS Snowball devices. The faster that drone imagery can be processed, the faster it can get to decision makers. 51 | 52 | 53 | ### Using Amazon SageMaker for geospatial imagery with Capella Space 54 | 55 | [Details](https://virtual.awsevents.com/media/0_36fs7wbw) 56 | 57 | Capella Space is leveraging Amazon SageMaker to build complex machine learning (ML) models on Synthetic Aperture Radar (SAR) satellite imagery. By applying ML to SAR imagery, Capella can begin automatically detecting global events in near-real time without being hindered by weather or time of day. Join this session to learn which approaches can be applied to geospatial use cases and learn architectures for labeling, training, and deploying geospatial ML models on AWS. 58 | 59 | 60 | ### How to use fully managed Jupyter notebooks in Amazon SageMaker 61 | 62 | [Details](https://virtual.awsevents.com/media/1_eaae4pj5) 63 | 64 | Managing compute instances to view, run, or share a notebook is tedious. Amazon SageMaker offers several choices to use Jupyter notebooks, including Amazon SageMaker Studio. SageMaker Studio notebooks are one-click Jupyter notebooks that you can spin up quickly. The underlying compute resources are fully elastic, so you can easily dial up or down the available resources, and the changes take place automatically in the background without interrupting your work. You can also easily share notebooks with others, making collaboration easy and scalable. In this session, see a demo of SageMaker Studio and other ways to use Jupyter notebooks for building machine learning models. 65 | 66 | 67 | ### Desbloqueando dados do espaço para resolver desafios do planeta Terra (Unlocking space data to solve challenges on planet Earth) 68 | 69 | [Details](https://virtual.awsevents.com/media/1_h2s21bx1) 70 | 71 | Os dados do espaço habilitam formas sem precedentes para monitorar, compreender e navegar nos dados em nosso planeta em constante mudança. Nesta sessão, apresentaremos toda a jornada do cliente. Demonstrando o pipeline de dados, incluindo a captura e o processamento de imagens geoespaciais usando AWS Ground Station, Sagemaker e Step Functions com base nas experiências de clientes na América Latina. A primeira parte desta apresentação será dedicada à AWS Ground Station, que permite que organizações públicas e privadas façam a ingestão de dados de satélites na AWS, permitindo acesso de baixa latência. A segunda parte da apresentação explorará os recursos de machine learning nas imagens de satélite extraindo insights de negócios endereçando desafios globais. 72 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | Note that https://github.com/jeffejefe/reinvent-geospatial is now more up to date. 2 | 3 | ## Geospatial Talks from AWS Summits and Events 4 | Talks, sessions and workshops that may be of interest to those working with geospatial data. PRs accepted! 5 | 6 | ### [2020 re:Invent](2020-reinvent.md) 7 | ### [2019 re:Invent](2019-reinvent.md) 8 | ### [2019 Public Sector Summit](2019-ps-summit.md) 9 | ### [2018 re:Invent](2018-reinvent.md) 10 | ### [2018 Public Sector Summit](2018-ps-summit.md) 11 | ### [2017 re:Invent](2017-reinvent.md) 12 | --------------------------------------------------------------------------------