Earth Observation Foundation Models for region-specific flood segmentation

AI foundation models for earth observation are an important tool to inform and adapt to extreme weather events brought on by climate change. Here, we investigate the performance of these models for a region-specific task. We build upon the Prithvi-EO model, which uses optical imagery, and incorporate Synthetic Aperture Radar (SAR) imagery for UK and Ireland by both additional pretraining and directly fine tuning for regional flood segmentation. Incorporating SAR band imagery via either approach improved flood segmentation performance from 0.58 to 0.79 (by approximately 35%), suggesting that EOFMs can relatively easily be tuned to new locations and application-specific satellite bands.