FLNet: Sharper satellite images for faster flood relief
When floods hit, getting relief to the right farms fast is hard. Manual surveys are slow and can miss the big picture, while free satellite images are often too blurry to pinpoint crop damage.
FLNet is a new AI approach that sharpens 10 m Sentinel-2 images to about 3 m using super-resolution, then maps which fields are fully damaged. Tested on India’s Bihar Flood Impacted Croplands Dataset (BFCD-22), it raised the crucial Full Damage F1-score from 0.83 to 0.89 - nearly matching the 0.89 score of costly commercial high-resolution imagery.
Why it matters: better, faster damage maps can guide fairer, more efficient relief and recovery at national scale and at a fraction of the cost.
Paper: https://arxiv.org/abs/2601.03884v1
Paper: https://arxiv.org/abs/2601.03884v1
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AI DisasterResponse Agriculture RemoteSensing SatelliteImagery Floods India
