Irrigation ML paper published

I’ve previously posted on this blog about a machine-learning model we built to detect irrigation globally.

I’m happy to report that our work is now published as a paper in the journal Advances in Water Resources.

A few other useful links to round this off:

• If paywalled, see this preprint
• Our talk at Google Geo for Good 2020 summit
• Results dataset are on Zenodo
• Source code is on Github
• Concepts and how we built it: this blog post

See also

• Large hailstorms in the US
• How we built a global ML model with Google Earth Engine
• Global Irrigation Map, our capstone machine learning project