Abstract: Cross-view image geo-localization (CVGL) is a key technique for achieving geographic location matching through multi-source images, with broad applications in fields such as robot navigation and autonomous driving. However, existing methods typically assume that images are directionally aligned, making them less effective in real-world scenarios with directional deviations. To address this challenge, this paper proposes a ranking aggregation-based method for enhancing directional robustness. By introducing random directional shifts to ground-level images and employing mean and min aggregation strategies, the proposed method improves the matching accuracy and robustness. Experiments conducted on the CVUSA_360 and CVACT_360 datasets demonstrate that this approach significantly enhances the performance of various models under directionally misaligned conditions, with particularly outstanding results in terms of the R@1 metric. Visual analysis further verifies the advantages of the method in strengthening directional robustness, suppressing noise, and improving matching accuracy, thus providing an efficient solution for geo-localization tasks in complex scenarios.

Key words: cross-view image geo-localization, directional misalignment, ranking aggregation, directional robustness