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GeoFMs are overconfident and equally fragile under stress, regardless of whether they are pretrained on EO or ImageNet data, challenging conventional evaluation practices.
Ditch mean pooling in your geospatial foundation models: richer pooling methods like GeM can boost accuracy by up to 5% and slash the geographic generalization gap by 40%.
