R2PLoc: A Region-to-Point UAV Visual Geo-Localization Framework Leveraging Hierarchical Semantic Representation

The challenges in uncrewed aerial vehicle (UAV) visual geo-localization primarily stem from discrepancies between satellite maps and aerial images, including scale variations, viewpoint deviations, and spatiotemporal mismatches. Current approaches adopt retrieval-based or keypoint-matching-based localization, and some studies employ a cascaded approach. However, these methods still exhibit limitations in addressing discrepancies. To address these challenges, we propose a region-to-point UAV visual geo-localization (R2PLoc) framework. Specifically, we consider UAV visual geo-localization as the process of retrieving corresponding regions from satellite map databases using aerial images while establishing a projective relationship between them. First, we employ a shared backbone network for semantic feature extraction to conserve computational resources. Then, the hierarchical semantic aggregation module (HSAM) is designed to address the feature distribution shifts by fusing multiscale semantics that combine both global contexts and local structures. Additionally, the semantic-enhanced hierarchical refinement matcher (SHRM) is constructed to improve the geometric consistency of keypoint matching by integrating high-level semantic information. Furthermore, the UAV-R2P dataset is constructed for the region-to-point geo-localization task. The qualitative and quantitative experimental results demonstrate that our method outperforms most state-of-the-art methods with similar model size on most available datasets.