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AWS Solutions Library

Guidance for Geospatial Data Enhancement for Agronomic Data Visualization on AWS

Overview

This Guidance helps customers import, process, and display geospatial imagery with Amazon SageMaker geospatial capability. By demonstrating how to use a geospatial capability in the agricultural use case, this is a starting point for customers looking to build an agronomic data platform.

How it works

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

Download the architecture diagram

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.

To improve operational efficiency, it is recommended you enable logging to Amazon CloudWatchfor each AWS service, as well as configure alarms, event notifications, and establish different subscriptions to events through Amazon Simple Notification Services (Amazon SNS). API Gateway can enable logging to CloudWatch to understand the API requests and backend responses from Lambda. CloudWatch logs enable the user to understand the system performance and if business outcomes are being achieved through successful end-user content consumption.

Read the Operational Excellence whitepaper 

Use Amazon Cognito with a user pool token type to authenticate and authorize for website and API access. Machine access uses AWS Identity and Access Management (IAM) roles with least-privilege access to Lambda and API Gateway. Public assets reside in private Amazon S3 buckets with CloudFront for reduced latency. CloudFront is the only permitted role to access Amazon S3. API Gateway is protected by Amazon Cognito for public user authentication and authorization.

Read the Security whitepaper 

Serverless technologies implemented in this Guidance are highly available and scalable depending on traffic. ElastiCache includes a topology that allows scaling vertically and horizontally. ElastiCache for Redis cluster allows you to scale horizontally. The synchronous application does not require a workflow to be automated with retries, since it does not rely on a timed resolution. Default log streams from Lambda processing jobs are implemented for monitoring. Backups are stored in Amazon S3 and can be used to restore ElastiCache clusters or launch in a new region.

There is no comingled customer data due to the solution being implemented in single accounts.

Read the Reliability whitepaper 

Services were selected to meet end user expectations and needs related to scalability, elasticity, cost performance, and serverless in mind.

ElastiCache offers native customizations to cache data according to user requirements; SageMaker can create and modify ML workloads; Lambda functions can be customized with additional application logging; and CloudWatch logs and reports can be customized.

Read the Performance Efficiency whitepaper 

This Guidance uses serverless technologies to process data, train models, and optimize costs by using pay-as-you-go pricing. The scaling behaviours of AWS managed services and API Gateway help reduce over-provisioning resources; you can maximize resource use and reduce the amount of energy needed to run a given workload. 

Read the Cost Optimization whitepaper 

To further enhance minimization of resources required, users have configuration options such caching types, reserved memory levels, and cluster replication strategies.

This guidance utilizes Sentinel-2 satellite imagery that is updated and made available on Open Data on AWS Data Exchange.

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.