GeoPhysAdapter: Scale-matched geophysical adaptation for cross-domain landslide mapping with vision foundation models

Newly triggered landslides rarely carry immediate annotations, so cross-domain transferability determines the value of landslide mapping for emergency response and regional risk assessment. Vision foundation models have strengthened representational transfer, yet on unseen regions, events, and sources they still generate high-confidence false alarms. Terrain, material, and rainfall triggering can constrain such errors, but their supports are local, regional, and event-scale, so resampling onto a 10 m grid misaligns them with the segmentation decision unit and compounds the uncertain geographic context problem (UGCoP). We propose GeoPhysAdapter, which anchors on a frozen vision foundation model, restricts terrain, material, and triggering to dense spatial guidance, regional modulation, and event-timing forcing, and applies bounded adaptation at two decision units, the pixel and the candidate landslide body, reverting exactly to the visual prediction where support is insufficient. On an event-isolated PILD dataset of four public sources, 55 global landslide events, and 7890 test samples, 70.3% of cross-domain false-positive mass lies in near-pure spurious bodies of median equivalent diameter 207 m, matching coarse-prior support rather than the pixel. Pixel-level adaptation removes a net 507,817 erroneous pixels, reducing error by 7.76%, whereas raising the decision unit to the candidate body, under identical samples, anchor, and baseline, increases error reduction to 23.99% and improves IoU by a relative 14.2%. The gain decreases monotonically with terrain displacement, exceeds a strong optical control of spectral change and visual confidence alone by 0.019 IoU, remains positive across five vision anchors, and survives a pre-registered re-execution with a 20.61% error reduction. The binding constraint is therefore the match between the native scale of physical priors and the operative decision unit, not denser physical stacking. Data and code are publicly available at https://github.com/Liu-Zhihang/geophysadapter .