Today, Radiant Earth Foundation announced the registration of a Spatio-Temporal Asset Catalog (STAC)-compliant version of SpaceNet’s high-quality geospatial labeled datasets for roads and buildings on Radiant MLHub. Radiant MLHub is the world’s first cloud-based open library dedicated to Earth observation training data for machine learning algorithms. The updated dataset catalog is also available through SpaceNet’s data registry.
Founded in 2016 to accelerate open source geospatial machine learning, SpaceNet is a nonprofit organization that runs data challenges and releases the training datasets, baseline algorithms, winning algorithms, and detailed evaluations under an open source license. They have organized six data challenges to date, each focusing on a different problem that applies machine learning to satellite imagery to solve complex mapping problems.