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AWS Solutions Library

Guidance for Running Machine Learning Algorithms with Satellite Data on AWS

Overview

This Guidance illustrates a comprehensive approach for ingesting, processing, and deriving insights from satellite imagery data. AWS Ground Station is used to ingest the satellite data, and Amazon SageMaker is used to label the image data, train machine learning (ML) models, and deploy the trained models. From ingestion to ML analysis, the trained models can be integrated into your applications or dashboards for analysis and visualization of the satellite data. This holistic approach streamlines the entire lifecycle of satellite data processing and analytics so you can quickly develop and deploy intelligent applications powered by your own satellite data.

How it works

This architecture diagram shows how to ingest satellite imagery through AWS Ground Station using Amazon SageMaker to label data, train ML models, and report inferences to your applications.

Download the architecture diagram PDF 

Well-Architected Pillars

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

This Guidance uses an event-driven application pipeline where processing is automated based on Amazon S3 events. This approach relies on Lambda functions, which are triggered whenever new data becomes available in Amazon S3, eliminating the need for scheduled batch processes. The inference results and associated metadata, including quality and accuracy metrics for the ML model, persist in DynamoDB. By using this event-driven architecture, you can automate your data processing pipelines, reducing manual intervention and enabling real-time processing as new data becomes available.

Read the Operational Excellence whitepaper

AWS Ground Station uses secure communication channels to transmit data between the ground stations and AWS. The data is encrypted in transit using industry-standard encryption protocols, such as TLS. Store your data in Amazon S3 and secure it from unauthorized access with encryption features and access management tools.

Read the Security whitepaper

Amazon S3 provides data durability and high availability, and Lambda is both a highly scalable and highly available compute resource that automatically scales up or down based on your workload demands. Each of these services can enhance reliability without additional overhead or management required from you or the user.

Read the Reliability whitepaper

SageMaker automatically captures and monitors various metrics during model training. These metrics can be key indicators to assist in rightsizing your infrastructure for future training jobs. In addition, Lambda automatically scales compute resources in response to incoming workloads for optimal utilization, eliminating the need to overprovision resources. Together, these services provide a managed experience and reduce the heavy lifting associated with orchestrating ML or compute tasks.

Read the Performance Efficiency whitepaper

The Amplify service provides a simplified approach to building and scaling web and mobile applications while also helping to control costs. Amplify offers a pay-as-you-go pricing model, allowing the hosting of multiple sites and providing free SSL certificates, so you only pay for the resources you actually consume. Furthermore, the AWS Ground Station service allows you to pay solely for the actual antenna time used and utilize the global footprint of ground stations to download data when and where needed, without long-term contracts or hidden fees. You can access any antenna in the global AWS Ground Station network at a single price.

Read the Cost Optimization whitepaper

The use of Lambda removes the requirement for provisioning and maintaining physical servers, thereby reducing the energy consumption associated with traditional compute infrastructure. Additionally, the lifecycle policies of Amazon S3 allow for the automatic transition of data to colder, more cost-effective storage tiers, optimizing storage costs and resource utilization. Furthermore, the AWS Ground Station service eliminates the need for you to build and maintain your own ground station infrastructure, which can be resource-intensive and inefficient.

Read the Sustainability whitepaper

Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.