We announced the Radiant Earth Spot the Crop Data Challenge winners to predict crop types in Western Cape, South Africa using satellite image time-series. We sat down with Raphael Kiminya, the winner of the track that used Sentinel-2 multispectral data as input to his model, and MG Ferreira and Tien-Dung LE that used both Sentinel-2 and Sentinel-1 (radar) data as input. Read how they've approached the challenge and watch the webinar recording where they dive deeper into their winning solutions.

Radiant Earth Foundation hosted an international expert workshop to discuss how best to use machine learning techniques on NASA’s Earth Observation data and address environmental challenges. This workshop was sponsored by the NASA Earth Science Data Systems (ESDS). 

• Read the Workshop Report (PDF) to learn about the challenges, potential solutions, and best practices for using EO data in ML workflows identified by expert participants 
• Watch the recorded expert presentations