STAC Machine Learning Model (MLM) Extension to describe ML models, their training details, and inference runtime requirements.

Overview of STAC Extensions, with advice on creating new extensions

Provides a way to describe point cloud datasets. The point clouds can come from either active or passive sensors, and data is frequently acquired using tools such as LiDAR or coincidence-matched imagery.

Provides additional fields relating to how the asset is stored in the cloud

Satellite related metadata for data collected from satellites.

An Item and Collection extension to describe machine learning (ML) models that operate on Earth observation data.

Provides a way to describe Items whose assets are in a geospatial projection.

Provide consumers with the information required to view an asset properly (e.g. on a online map)

Covers electro-optical data that represents a snapshot of the Earth. It could consist of cloud cover and multiple spectral bands, for example visible bands, infrared bands, red edge bands and panchromatic bands.