Date: 2020-08-10
Accepted
We define the initial use case for ml-aoi spec that exposes assumptions and reasoning for specific layout choices:
providing training data source for Raster Vision model training process.
ml-aoi STAC Items represent a reified relation between feature rasters and ground-truth label in a machine learning
training dataset.
Each ml-aoi Item roughly correspond to a "scene" or a training example.
Current known STAC extensions are not suitable for this purpose. The closest match is the STAC label extension.
label extension provides a way to define either vector or raster labels over area.
However, it does not provide a mechanism to link those labels with feature images;
links with rel type source point to imagery from which labels were derived.
Sometimes this imagery will be used as feature input for model training, but not always.
The concept of source label imagery and input feature imagery are semantically distinct.
For instance, it is possible to apply a single source of ground-truth building labels to train a model on either
Landsat or Sentinel-2 scenes.
ml-aoi Item links to both raster STAC item and label STAC item.
In this relationship the source raster and label items are static and long-lived, being used by several ml-aoi
catalogs. By contrast ml-aoi catalog is somewhat ephemeral, it captures the training set in order to provide model
reproducibility and provenance.
There can be any number of ml-aoi catalogs linking to the same raster and label items, while varying selection,
training/testing/validation split and class configuration.
We will adopt the use and development of ml-aoi extension in future machine-learning projects.
We will no longer attempt to use label extension as a sole source of training data for ML models.
We will continue development of tools to both produce and consume ml-aoi extension catalogs.