Progressive Pseudo-Label Filtering with Reciprocal Neighborhood Retrieval for Cross-View Geo-Localization

Cross-view geo-localization retrieves the satellite image corresponding to a ground-level query. Supervised methods require paired ground–satellite annotations, whereas the fully unsupervised setting must learn from two image collections without paired ground truth. We present progressive pseudo-label filtering with reciprocal neighborhood retrieval (PPLR), a two-stage pipeline in which cold-start initialization is followed by progressive pseudo-label learning. Reciprocal neighborhood retrieval expands candidates beyond strict top-1 matching; margin-based coarse filtering controls the retained quantity; and consistency-guided fine filtering refines quality using augmentation stability, retrieval margins, and assignment history. Under the 0% paired-ground-truth protocol, PPLR reaches Recall@1 of 96.30 on CVUSA Test, 89.23 on CVACT Val, and 66.84 on the CVACT Test split. Source-only transfer gives Recall@1 of 73.06, 41.35, and 58.79 in the CVUSA → CVACT Val, CVUSA → CVACT Test, and CVACT → CVUSA directions, respectively. These results provide evidence of partial transferability while a domain gap remains.