Machine Learning on Earth Observation

Article by: Charles Mwangi- Kenya Space Agency & Louisa Nakanuku-Diggs- Radiant Earth

This dataset merges Google’s V3 Open Buildings and Microsoft’s latest Building Footprints, making it the most complete openly available…

This dataset is accessible to the entire remote sensing community on Source Cooperative.

We hosted a machine learning competition to develop models that can accurately detect small farm field boundaries in satellite images…

An interview with Muhamed Tuo, Data Scientist and Member of the Winning Team of the AgriFieldNet Data Challenge.

It is time to say goodbye to our monthly industry round-up newsletter, the ML4EO Market News.

Three inventions we’re working on at Radiant Earth Foundation

How Radiant MLHub strengthens the data collection to analytics pipeline for agriculture projects.

A guide to identifying croplands with reasonable accuracy using a semantic segmentation model.

The call for nominations is open to any individual or team building agricultural-related applications for Africa

The models include metadata based on the STAC ML Model Extension to enable easy sharing and retrieval.

On December 13–17, Radiant Earth’s team will present their latest research and milestones at the American Geophysical Union (AGU) Fall…

Describing ML Models with the Geospatial Machine Learning Model Catalog (GMLMC)

Using STAC to catalog machine learning training data.

Submissions to the new Datasets and Benchmarks track require data documentation and availability on an open repository.