TopicGeo: An Efficient Unified Framework for Geolocation

Abstract

Vision-based geolocation techniques that establish spatial correspondences between smaller query images and larger georeferenced images have gained significant attention. Existing approaches typically employ a separate "retrieve-then-match" paradigm, whereas such paradigms suffer from computational inefficiency or precision limitations. To this end, we propose TopicGeo, a unified framework for direct and precise query-to-reference image matching via three key innovations. The textual object semantics, called topics, distilled from CLIP prompt learning are embedded into the geolocation framework to eliminate intra-class and inter-class distribution discrepancies while also enhancing processing efficiency. Center-based adaptive label assignment and outlier rejection mechanisms as a joint retrieval-matching optimization strategy ensure task-coherent feature learning and precise spatial correspondences. A multi-level fine matching pipeline is introduced to refine matching from quality and quantity. Evaluations on large-scale synthetic and real-world datasets illustrate that TopicGeo achieves state-of-the-art performance in retrieval recall and matching accuracy while maintaining a balance in computational efficiency.