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At AWS re:Invent 2022, we previewed Amazon SageMaker geospatial capabilities, permitting information scientists and machine studying (ML) engineers to construct, practice, and deploy ML fashions utilizing geospatial information. Geospatial ML with Amazon SageMaker helps entry to available geospatial information, purpose-built processing operations and open supply libraries, pre-trained ML fashions, and built-in visualization instruments with Amazon SageMaker’s geospatial capabilities.
During the preview, we had a number of curiosity and nice suggestions from prospects. Today, Amazon SageMaker geospatial capabilities are usually obtainable with new safety updates and extra pattern use instances.
Introducing Geospatial ML options with SageMaker Studio
To get began, use the fast setup to launch Amazon SageMaker Studio within the US West (Oregon) Region. Make certain to make use of the default Jupyter Lab 3 model whenever you create a brand new person within the Studio. Now you possibly can navigate to the homepage in SageMaker Studio. Then choose the Data menu and click on on Geospatial.
Here is an summary of three key Amazon SageMaker geospatial capabilities:
- Earth Observation jobs – Acquire, rework, and visualize satellite tv for pc imagery information utilizing purpose-built geospatial operations or pre-trained ML fashions to make predictions and get helpful insights.
- Vector Enrichment jobs – Enrich your information with operations, reminiscent of changing geographical coordinates to readable addresses.
- Map Visualization – Visualize satellite tv for pc photos or map information uploaded from a CSV, JSON, or GeoJSON file.
You can create all Earth Observation Jobs (EOJ) within the SageMaker Studio pocket book to course of satellite tv for pc information utilizing purpose-built geospatial operations. Here is a listing of purpose-built geospatial operations which might be supported by the SageMaker Studio pocket book:
- Band Stacking – Combine a number of spectral properties to create a single picture.
- Cloud Masking – Identify cloud and cloud-free pixels to get improved and correct satellite tv for pc imagery.
- Cloud Removal – Remove pixels containing elements of a cloud from satellite tv for pc imagery.
- Geomosaic – Combine a number of photos for higher constancy.
- Land Cover Segmentation – Identify land cowl sorts reminiscent of vegetation and water in satellite tv for pc imagery.
- Resampling – Scale photos to completely different resolutions.
- Spectral Index – Obtain a mixture of spectral bands that point out the abundance of options of curiosity.
- Temporal Statistics – Calculate statistics by time for a number of GeoTIFFs in the identical space.
- Zonal Statistics – Calculate statistics on user-defined areas.
A Vector Enrichment Job (VEJ) enriches your location information by purpose-built operations for reverse geocoding and map matching. While you should use a SageMaker Studio pocket book to execute a VEJ, you possibly can view all the roles you create utilizing the person interface. To use the visualization within the pocket book, you first have to export your output to your Amazon S3 bucket.
- Reverse Geocoding – Convert coordinates (latitude and longitude) to human-readable addresses.
- Map Matching – Snap inaccurate GPS coordinates to street segments.
Using the Map Visualization, you possibly can visualize geospatial information, the inputs to your EOJ or VEJ jobs in addition to the outputs exported out of your Amazon Simple Storage Service (Amazon S3) bucket.
Security Updates
At GA, we’ve got two main safety updates—AWS Key Management Service (AWS KMS) for buyer managed AWS KMS key assist and Amazon Virtual Private Cloud (Amazon VPC) for geospatial operations within the buyer Amazon VPC setting.
AWS KMS buyer managed keys provide elevated flexibility and management by enabling prospects to make use of their very own keys to encrypt geospatial workloads.
You can use KmsKeyId to specify your personal key in BeginEarthObservationJob and BeginVectorEnrichmentJob as an non-compulsory parameter. If the shopper doesn’t present KmsKeyId, a service owned key might be used to encrypt the shopper content material. To be taught extra, see SageMaker geospatial capabilities AWS KMS Support within the AWS documentation.
Using Amazon VPC, you have got full management over your community setting and might extra securely hook up with your geospatial workloads on AWS. You can use SageMaker Studio or Notebook in your Amazon VPC setting for SageMaker geospatial operations and execute SageMaker geospatial API operations by an interface VPC endpoint in SageMaker geospatial operations.
To get began with Amazon VPC assist, configure Amazon VPC on SageMaker Studio Domain and create a SageMaker geospatial VPC endpoint in your VPC within the Amazon VPC console. Choose the service identify as com.amazonaws.us-west-2.sagemaker-geospatial and choose the VPC during which to create the VPC endpoint.
All Amazon S3 assets which might be used for enter or output in EOJ and VEJ operations ought to have web entry enabled. If you don’t have any direct entry to these Amazon S3 assets by way of the web, you possibly can grant SageMaker geospatial VPC endpoint ID entry to it by altering the corresponding S3 bucket coverage. To be taught extra, see SageMaker geospatial capabilities Amazon VPC Support within the AWS documentation.
Example Use Case for Geospatial ML
Customers throughout varied industries use Amazon SageMaker geospatial capabilities for real-world purposes.
Maximize Harvest Yield and Food Security
Digital farming consists of making use of digital options to assist farmers optimize crop manufacturing in agriculture by using superior analytics and machine studying. Digital farming purposes require working with geospatial information, together with satellite tv for pc imagery of the areas the place farmers have their fields positioned.
You can use SageMaker to determine farm subject boundaries in satellite tv for pc imagery by pre-trained fashions for land cowl classification. Learn about How Xarvio accelerated pipelines of spatial information for digital farming with Amazon SageMaker Geospatial within the AWS Machine Learning Blog. You can discover an end-to-end digital farming instance pocket book by way of the GitHub repository.
Damage Assessment
As the frequency and severity of pure disasters improve, it’s necessary that we equip decision-makers and first responders with quick and correct harm evaluation. You can use geospatial imagery to foretell pure catastrophe harm and geospatial information within the speedy aftermath of a pure catastrophe to quickly determine harm to buildings, roads, or different crucial infrastructure.
From an instance pocket book, you possibly can practice, deploy, and predict pure catastrophe harm from the floods in Rochester, Australia, in mid-October 2022. We use photos from earlier than and after the catastrophe as enter to its educated ML mannequin. The outcomes of the segmentation masks for the Rochester floods are proven within the following photos. Here we are able to see that the mannequin has recognized places throughout the flooded area as possible broken.
You can practice and deploy a geospatial segmentation mannequin to evaluate wildfire damages utilizing multi-temporal Sentinel-2 satellite tv for pc information by way of GitHub repository. The space of curiosity for this instance is positioned in Northern California, from a area that was affected by the Dixie Wildfire in 2021.
Monitor Climate Change
Earth’s local weather change will increase the danger of drought resulting from world warming. You can see methods to purchase information, carry out evaluation, and visualize the adjustments with SageMaker geospatial capabilities to watch shrinking shoreline attributable to local weather change within the Lake Mead instance, the biggest reservoir within the US.
You can discover the pocket book code for this instance within the GitHub repository.
Predict Retail Demand
The new pocket book instance demonstrates methods to use SageMaker geospatial capabilities to carry out a vector-based map-matching operation and visualize the outcomes. Map matching lets you snap noisy GPS coordinates to street segments. With Amazon SageMaker geospatial capabilities, it’s attainable to carry out a VEJ for map matching. This kind of job takes a CSV file with route info (reminiscent of longitude, latitude, and timestamps of GPS measurements) as enter and produces a GeoJSON file that accommodates the anticipated route.
Support Sustainable Urban Development
Arup, one in every of our prospects, makes use of digital applied sciences like machine studying to discover the affect of warmth on city areas and the elements that affect native temperatures to ship higher design and assist sustainable outcomes. Urban Heat Islands and the related dangers and discomforts are one of many greatest challenges cities are going through at the moment.
Using Amazon SageMaker geospatial capabilities, Arup identifies and measures city warmth elements with earth commentary information, which considerably accelerated their capability to counsel shoppers. It enabled its engineering groups to hold out analytics that weren’t attainable beforehand by offering entry to elevated volumes, sorts, and evaluation of bigger datasets. To be taught extra, see Facilitating Sustainable City Design Using Amazon SageMaker with Arup in AWS buyer tales.
Now Available
Amazon SageMaker geospatial capabilities are actually usually obtainable within the US West (Oregon) Region. As a part of the AWS Free Tier, you will get began with SageMaker geospatial capabilities without spending a dime. The Free Tier lasts 30 days and consists of 10 free ml.geospatial.interactive compute hours, as much as 10 GB of free storage, and no $150 month-to-month person payment.
After the 30-day free trial interval is full, or when you exceed the Free Tier limits outlined above, you pay for the elements outlined on the pricing web page.
To be taught extra, see Amazon SageMaker geospatial capabilities and the Developer Guide. Give it a try to ship suggestions to AWS re:Post for Amazon SageMaker or by your normal AWS assist contacts.
– Channy