UAV-GeoLoc: A Large-Vocabulary Dataset and Geometry-Transformed Method for UAV Geo-Localization

Visual geo-localization empowers unmanned aerial vehicles (UAVs) to navigate in Global Navigation Satellite System (GNSS)-denied environments. This capability is typically formulated as an image retrieval task, where UAV-acquired images are matched against geo-tagged satellite image patches. However, existing datasets are predominantly limited to single urban scenarios and often assume an ideal spatial alignment between UAV and satellite, which is unrealistic in real-world settings. To address these limitations, we present World-UAV, a large-vocabulary dataset that offers: (1) extensive scene diversity, spanning 27 unique geographical categories, and (2) realistic spatial discrepancies, incorporating significant geometric variations in rotation and scale between UAV and satellite image pairs. These geometric transformations pose substantial challenges to current global descriptor-based methods, which exhibit marked performance degradation in such scenarios. We propose UAVPlace, a novel learning approach incorporating geometric transformation encoding modules to integrate multi-perspective transformation features, thereby generating transformation-invariant global descriptors. Extensive experiments demonstrate the effectiveness of our method.