AI4G Flood

Sentinel-1 carries a radar, which sees the ground through cloud and at night.⁴ This model takes a Sentinel-1 radar image from before a possible flood and one from during it, and marks the pixels that turned to water.^1,2 It is a U-Net, a common network for labelling every pixel of an image, built on a small MobileNetV2 encoder.¹

The model comes from the Microsoft AI for Good Lab.^2,4 Amit Misra, Kevin White, Simone Fobi Nsutezo and Juan Lavista of the lab, with William Straka III of the University of Wisconsin-Madison, published it in Nature Communications on 1 July 2025.^2,3 They ran the model over every Sentinel-1 image from October 2014 to September 2024 to build a global record of floods.^2,4 The paper uses it to find areas of Ethiopia that flood again and again, and to map the May 2024 floods in Kenya as they happened.²

The scripts run in two ways. Give a country and a date range, and the script pulls processed Sentinel-1 images from the Microsoft Planetary Computer and writes one GeoTIFF map per scene, which a second script merges into one.¹ Or download a before and after pair yourself, for example from the Alaska Satellite Facility.¹ Those images need the same processing as the training data, or the results may differ.¹ The trained model is a file of about 60 MB in the repository.⁵

The ten-year record is a download of its own on Hugging Face.⁴ It holds detections at 20 metres in 3 by 3 degree tiles, with soil moisture, elevation, temperature and land cover alongside each one for weeding out false alarms.⁴ Recurrence maps count the months in which each place flooded.⁴ For a model of a place, that is a decade of observed floods to set against a flood model's predictions.

The model mistakes permanent lakes and rivers for flood unless they are masked out with a map of standing water such as ESA WorldCover or Global Surface Water.¹ The paper's method widens each detection by 80 metres.^1,2 The repository, trained model included, and the dataset are under the MIT licence.^1,4 ML4Floods does the same job from optical images.