ML AOI Extension

• Title: ML AOI
• Identifier: https://stac-extensions.github.io/ml-aoi/v0.1.0/schema.json
• Field Name Prefix: ml-aoi
• Scope: Item, Collection
• Extension Maturity Classification: Proposal
• Owner: @echeipesh @kbgg @duckontheweb

This document explains the ML AOI Extension to the SpatioTemporal Asset Catalog (STAC) specification.

An Item and Collection extension to provide labeled training data for machine learning models. This extension relies on but is distinct from the existing label extension. STAC items using the label extension link label assets with the source imagery for which they are valid, often as result of human labelling effort. By contrast STAC items using ml-aoi extension link label assets with raster items for each specific machine learning model that is being trained.

In addition to linking labels with feature items the ml-aoi extension addresses some of the common configurations for ML workflows. The use of this extension is intended to make the model training process reproducible as well as providing model provenance once the model is trained.

Item Properties and Collection Fields

Field Name	Type	Description
ml-aoi:split	string	Assigns item to one of train, test, or validate sets

Additional Field Information

ml-aoi:split

This field is optional. If not provided, it is expected that the split property will be added later before consuming the items.

bbox and geometry

• ml-aoi Multiple items may reference the same label and image item by scoping the bbox and geometry fields. TODO: Better describe scoping of overlap between raster and label items?
• ml-aoi Items bbox field may overlap when they belong to different ml-aoi:split set.
• ml-aoi Items in the same Collection should never have overlapping geometry fields.

Links

ml-aoi Item must link to both label and raster STAC items valid for its area of interest. These Link objects should set rel field to derived_from for both label and feature items.

ml-aoi Item should be contain enough metadata to make it consumable without the need for following the label and feature link item links. In reality this may not be practical because the use-case may not be fully known at the time the Item is generated. Therefore it is critical that source label and feature items are linked to provide the future consumer the option to collect additional metadata from them.

Field Name	Type	Name	Description
ml-aoi:role	string	Role	label or feature

Labels

An ml-aoi Item must link to exactly one STAC item that is using label extension. Label links should provide ml-aoi:role field set to label value.

Features

An ml-aoi Item must link to at least one raster STAC item. Feature links should provide ml-aoi:role field set to feature value.

Linked feature STAC items may use eo but that is not required. It is up to the consumer of ml-aoi Items to decide how to use the linked feature rasters.

Assets

Item should directly include assets for label and feature rasters.

Field Name	Type	Name	Description
ml-aoi:role	string	Role	label or feature
ml-aoi:reference-grid	bool	Reference Grid	This raster provides reference pixel grid for model training
ml-aoi:resampling-method	string	Resampling Method	Resampling method for non-reference-grid feature rasters

Resampling method should be one of the values supported by gdalwarp

Labels

Assets for the label item can be copied directly from the label item with their asset name preserved. Label assets should provide ml-aoi:role field set to label value.

Features

Assets for the raster item can be copied directly from the label item with their asset name preserved. Feature assets should provide ml-aoi:role field set to feature value.

When multiple raster features are included their resolutions and pixel grids are not likely to align. One raster may be specify ml-aoi:reference-grid field set to true to indicate that all other features should be resampled to match its pixel grid during model training. Other raster assets should be resampled to the reference pixel grid.

Collection

All ml-aoi Items should belong to a Collection that designates a specific model training input. There is one-to-one mapping between a single ml-aoi collection and a machine-learning model.

Collection fields

The consumer of ml-aoi catalog needs to understand the available label classes and features without crawling the full catalog. When member Items include multiple feature rasters it is possible that not all of them will overlap every AOI.

Contributing

All contributions are subject to the STAC Specification Code of Conduct. For contributions, please follow the STAC specification contributing guide Instructions for running tests are copied here for convenience.

Running tests

The same checks that run as checks on PR's are part of the repository and can be run locally to verify that changes are valid. To run tests locally, you'll need npm, which is a standard part of any node.js installation.

First you'll need to install everything with npm once. Just navigate to the root of this repository and on your command line run:

npm install

Then to check markdown formatting and test the examples against the JSON schema, you can run:

npm test

This will spit out the same texts that you see online, and you can then go and fix your markdown or examples.

If the tests reveal formatting problems with the examples, you can fix them with:

npm run format-examples

Design Decisions

Central choices and rational behind them is outlined in the ADR format:

ID	ADR
0002	Use Case
0003	Test/Train/Validation Split
0004	Sourcing Multiple Label Items