Increasing shared understanding of our world by expanding access to geospatial data and machine learning models.
Our initiatives
Radiant MLHub
Radiant MLHub is the world’s first cloud-based open library dedicated to Earth observation training data for use with machine learning algorithms.
Access open datasets and machine learning models from NASA, Planet, University of Maryland and others at https://mlhub.earth
STAC
The SpatioTemporal Asset Catalogs (STAC) metadata specification is a common language to describe geospatial information to make it more accessible and interoperable.
Learn more about STAC at https://stacspec.org/
Machine Learning Challenges
We help organize challenges to create capacity building opportunities and accelerate development of machine learning models to interpret Earth observation data.
Learn about our current challenge to detect field boundaries in Rwanda.
Our blog
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Last Issue of our Monthly ML4EO Market News
It is time to say goodbye to our monthly ML4EO news round-up. In this last issue, we invite you to explore a curated list of resources of the best sites to stay informed and give a hint of what’s next for our newslettter.
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New White Paper on Data Policies in Agriculture
We’ve published a new white paper that explores a data ethics framework to preemptively address farmers’ concerns in low- and middle-income countries. The paper is part of our work on the Enabling Crop Analytics at Scale (ECAAS) initiative of the Bill & Melinda Gates Foundation.
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Democratizing Open Machine Learning Technologies for Earth Observation
Three inventions we’re working on at Radiant Earth Foundation.