Beyond Top-1: Geographic Associations for Multi-Reference Cross-View Geo-Localization

Large-scale cross-view geo-localization typically follows a retrieval–localization paradigm: candidates are retrieved from an aerial gallery, and the Top-1 reference is selected for local localization. Yet an aerial gallery provides discrete observations of continuous geographic space, and several highly ranked candidates may jointly point to the same local region. Retaining only Top-1 severs geographic associations among candidates and discards complementary spatial localization evidence. We therefore propose GeoHypLoc, a framework for geographically associated multi-reference cross-view localization. It combines query visual support with geographic adjacency in the gallery to organize highly ranked candidates into a positively spatially associated reference set $H^{\ast }$. Preserving each reference's independent georeferencing, it maps their localization evidence into a common real-world geographic space and adaptively aggregates multi-reference evidence throughout coarse-to-fine localization. Finally, it combines region-level visual support with localization-derived spatial evidence to correct the localization response, shifting from single-reference matching to joint constraints over a geographic region. In complete retrieval–localization evaluation on VIGOR, GeoHypLoc improves Acc@3/5/10m over Sample4Geo+CCVPE by 6.60/5.90/4.23 percentage points on Same-area and 2.07/2.14/3.13 percentage points on Cross-area. These results show that organizing multi-reference localization evidence through geographic spatial associations among retrieved candidates can improve large-scale fine-grained cross-view geo-localization.