TY  - EJOU
AU  - Yu, Long 
AU  - Zhu, Ma 
AU  - Wang, Xu 
AU  - Pei, Yang 
AU  - Yang, Chunfang 

TI  - Cross-View Geo-Localization via Dynamic Multi-Positive Mining from Unlabeled Data
T2  - Computers, Materials \& Continua

PY  - 2026
VL  - 89
IS  - 2
SN  - 1546-2226

AB  - Cross-view geo-localization (CVGL) estimates the location of a street-level image by retrieving its matching GPS-tagged satellite tile. Semi-supervised methods reduce the need for dense annotations by mining pseudo labels, but most of them keep only one positive reference for each query. In real-world galleries, several overlapping satellite tiles may cover the same ground location. As a result, valid matches can be discarded as negatives, which gives the model conflicting supervision. To address this problem, we propose DMP-Geo, a semi-supervised cross-view geo-localization method that mines multiple positives for each query from unlabeled data. A bird’s-eye fusion encoder is designed to combine each panorama with its bird’s-eye-view projection, thereby reducing the viewpoint gap between ground and satellite images. A breakpoint-based mining strategy is then proposed to retain multiple top-ranked candidates before the first clear similarity drop and verify them through mutual consistency. Finally, a confidence-weighted multi-positive contrastive loss is introduced to assign different weights to the mined positives according to their confidence. With only 10%–30% of the annotations, DMP-Geo consistently outperforms the existing semi-supervised baseline on CVUSA, CVACT, and VIGOR, and demonstrates strong generalization to unseen cities.
KW  - Cross-view geo-localization; semi-supervised learning; multi-positive pseudo-labels; contrastive learning

DO  - 10.32604/cmc.2026.087636