Radiant MLHub is an open library for geospatial training data (and soon machine learning models) to advance machine learning applications on Earth Observations. It serves as a resource for a community of practice, giving data scientists benchmarks they can use to train and validate their models and improve its performance.
Radiant MLHub hosts open training datasets generated by Radiant Earth Foundation's team as well as other training data catalogs contributed by Radiant Earth’s partners. Radiant MLHub is open to anyone to access, store, register and/or share their training datasets for high-quality Earth observations. All of the training datasets are stored using a SpatioTemporal Asset Catalog (STAC) compliant catalog, and exposed through a common API.
Radiant MLHub Python Client was released in March 2021. You can use the client to search data on Radiant MLHub and easily download them to your Python environment. Checkout our tutorials repository on GitHub for Jupyter Notebook examples for using the Python Client.
Radiant Earth is developing the Geospatial Machine Learning Model Catalog (GMLMC) specification. GMLMC will empower repositories of ML models that users can access to find existing ML models for various geospatial applications. If you are interested in this topic, checkout GMLMC on GitHub to explore the ways you can contribute to it.
Join Radian MLHub community on Slack to be part of this collaborative open source group. If you are looking for new training data or models, or are interested in collaborating with us to develop new ones, get in touch!
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