GeoCoRe: Recovering Missing Cross-View Relations under Disjoint Pairwise Supervision for Tri-View Geo-Localization

Tri-view geo-localization integrates UAV, satellite, and ground views, with fully supervised settings typically relying on direct paired supervision for the cross-view relations of interest. Such supervision is difficult to obtain when geospatial data are independently collected across platforms or datasets, leaving some relations unobserved during training. This work considers an incomplete tri-view setting by recovering the UAV–ground (u–g) relation from UAV–satellite (u–s) and satellite–ground (s–g) pairs, without direct u–g supervision or shared location instances between the two observed relations. Existing pairwise methods can learn both observed relations effectively yet still struggle to recover the missing u–g relation. Because the two observed relations are supervised on disjoint location sets, each pairwise objective constrains only its own relation, and the shared satellite view alone does not establish cross-edge comparability. This motivates organizing the two relations under shared coordinates and a common metric. GeoCoRe is introduced as a two-stage framework for recovering missing cross-view relations in tri-view geo-localization. Stage 1 induces shared Geo-primitives as a shared primitive reference, while Stage 2 uses this reference to guide coordinate assignment and align shared-coordinate representations across the observed edges. GeoCoRe improves the average missing-edge R@1 from 1.53% for the strongest baseline to 6.11%, demonstrating the value of cross-relation compositionality for recovering unobserved relations. Code is available at https://anonymous.4open.science/r/GeoCoRe.