Radiant MLHub

ML Model Catalogs

Defining metadata for model cataloging

Radiant MLHub is beyond just a data repository. We think of Radiant MLHub as a set of commons to advance applications of ML on Earth observations. Therefore, we aim to expand its services to answer the needs of the community in this respect. One such need is a library of existing ML models that users can easily find and put into practice (either for inference or using it as a pre-trained model).

While examples of such model catalogs within the ML ecosystem exist, they do not support metadata related to geospatial ML models. For example, one might be interested in models that detect surface water at a specific spatial resolution or a model trained on data from a certain geographical region. Therefore, we are developing a geospatial ML model catalog that users can 1) register and publicly publish their models and 2) search for existing models using various query parameters. This catalog would require a standard definition for model metadata that we will develop in consultation with various groups in all community sectors. Read more... Geospatial Models Now Available in Radiant MLHub

ML Model STAC Extension

Radiant Earth is developing the ML Model Extension to the SpatioTemporal Asset Catalog (STAC) specification. The ML Model Extension will empower users to discover and access existing repositories of ML models for various geospatial applications. Explore the ML Model Extension on GitHub to discover the ways you can contribute to it.