This Guidance helps customers observe land use changes using geospatial data to support supply chain best practices. Customers can monitor changes in forest density using an Amazon SageMaker geospatial capability that simplifies the process of analyzing satellite images for changes in vegetation. Results are stored, cataloged, and observable on Amazon QuickSight for customers to review.

Architecture Diagram

Download the architecture diagram PDF 

Well-Architected Pillars

The AWS Well-Architected Framework helps you understand the pros and cons of the decisions you make when building systems in the cloud. The six pillars of the Framework allow you to learn architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable systems. Using the AWS Well-Architected Tool, available at no charge in the AWS Management Console, you can review your workloads against these best practices by answering a set of questions for each pillar.

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 takes in a supplier location using GeoJSON, and processes geospatial imagery for that location to observe changes in vegetation which would be indicative of deforestation. This output is stored as both JSON and PNG files, so end users (Procurement and Sustainability teams) can interrogate the data to observe changes in vegetation with QuickSight

    Read the Operational Excellence whitepaper 
  • This Guidance uses role-based access with Amazon Identity and Access Management (AWS IAM) and the Amazon S3 bucket has encryption enabled, is private, and blocks public access. The data catalog in AWS Glue has encryption enabled. All roles are defined with least-privilege, and all communications between services stay within the customer account. Administrators can control the notebook, and Athena and QuickSight data access through AWS IAM roles.

    Read the Security whitepaper 
  • AWS Glue, Amazon S3, and Athena are all serverless, and will scale data access performance as your data volume increases. Athena is serverless, so you can quickly query your data without having to set up and manage any servers or data warehouses.

    Read the Reliability whitepaper 
  • By using serverless technologies, you provision only the exact resources you use. Athena automatically runs queries in parallel, so most results come back within seconds, which means Sustainability and Procurement team end users can quickly get information about supplier locations. 

    Read the Performance Efficiency whitepaper 
  • This Guidance uses serverless components such as AWS Glue, Amazon S3, and Athena, services that automatically scale up and down to meet demand, so you only pay for what you use. 

    Read the Cost Optimization whitepaper 
  • With the exception of the SageMaker notebook (which will be set at a specific size), all other resources in this workload are serverless and scale with use, which reduces idle resources. Lifecyle configurations can be used to conclude the SageMaker notebook after inactivity. 

    Read the Sustainability whitepaper 

Implementation Resources

The sample code is a starting point. It is industry validated, prescriptive but not definitive, and a peek under the hood to help you begin. 

AWS Machine Learning
Blog

Remote Monitoring of Raw Material Supply Chains for Sustainability with Amazon SageMaker Geospatial Capabilities

This post demonstrates how to use SageMaker geospatial capabilities to easily baseline and monitor the vegetation type and density of areas where suppliers operate.

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.