ICL Characterization of Geo-Foundation Models

Geo-foundation models (GeoFMs) have emerged as powerful tools for remote sensing, yet there exists no theoretical framework characterizing which downstream tasks admit efficient few-shot adaptation. We address this gap by applying the in-context learning (ICL) complexity framework to classify remote sensing tasks as either ICL-Easy or ICL-Hard. We formalize six core remote sensing function classes—spectral classification, semantic segmentation, object localization, dense change detection, sparse change localization, and spatial interpolation—with explicit sufficient statistic structure. For ICL-Easy tasks (classification, segmentation, dense change detection, spatial interpolation), we prove that additive…