Open Repository for Geospatial Training Data Released

Caitlin Dempsey

Updated:

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.  

What is Radiant MLHub?

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).

Logo for Radian MLHub with brightly colored circles of different sizes within a large circle.

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.


Free weekly newsletter

Fill out your e-mail address to receive our newsletter!
Email:  

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.”

Related

Photo of author
About the author
Caitlin Dempsey
Caitlin Dempsey is the editor of Geography Realm and holds a master's degree in Geography from UCLA as well as a Master of Library and Information Science (MLIS) from SJSU.