FairRSFM evaluates three remote sensing foundation models by biome group and finds aggregate metrics mask ecological performance gaps
The authors introduce FairRSFM, which maps georeferenced samples from four remote sensing downstream datasets (m-EuroSAT, m-BigEarthNet, m-SA-Crop-Type, and MMEarth20K) to 14 terrestrial biome classes consolidated into six macro-groups, and evaluates Prithvi-EO-2.0, SatMAE, and DOFA under a unified frozen-backbone protocol across three random seeds, showing that aggregate metrics consistently mask biome-dependent disparities, for example Prithvi-EO-2.0 reaching high overall macro-F1 on m-EuroSAT while its worst-group score is markedly lower, and compares BOLP, DBR, and GroupDRO as mitigation baselines whose effectiveness is model- and task-dependent.