Open Repository for Geospatial Training Data Released

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Radiant Earth has launched Radiant MLHub, a cloud-based open library for training geospatial data used by machine learning algorithms.  In launching the repository, Radiant Earth noted that while there is an abundance of satellite imagery, there is a lack of training data and tools to train machine learning algorithms.  Radiant MLHub is a federated site for the discovery and access of high-quality Earth observation (EO) training datasets and machine learning models.  Individuals and organizations can contribute by sharing their own training data and models with Radiant MLHub.  The data and models available on Radiant MLHub are distributed under a Creative Commons license (CC BY 4.0).

The site debuted with “crop type” training data for major crops in Kenya, Tanzania and Uganda supplied by the Radiant Earth Foundation.  Future planned datasets include Global Land Cover and Surface Water as well as additions from the site’s partners. All of the datasets are stored using a SpatioTemporal Asset Catalog (STAC) compliant catalog. Per Radiant Earth: “Training datasets include pairs of imagery and labels for different types of ML problems including image classification, object detection, and semantic segmentation.”

Users interested in accessing the site’s data and models can get started by downloading this how-to-guide.

Visit: Radiant MLHub

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