Enhancing Cross-View UAV Geolocalization via LVLM-Driven Relational Modeling
This work provides an incremental improvement in UAV geolocalization accuracy for applications requiring precise drone image positioning.
This paper tackles the problem of cross-view UAV geolocalization by aligning drone imagery with satellite databases. The proposed method, using a plug-and-play ranking architecture and an LVLM, significantly boosts retrieval accuracy across various baseline architectures and standard benchmarks, even under demanding conditions.
The primary objective of cross-view UAV geolocalization is to identify the exact spatial coordinates of drone-captured imagery by aligning it with extensive, geo-referenced satellite databases. Current approaches typically extract features independently from each perspective and rely on basic heuristics to compute similarity, thereby failing to explicitly capture the essential interactions between different views. To address this limitation, we introduce a novel, plug-and-play ranking architecture designed to explicitly perform joint relational modeling for improved UAV-to-satellite image matching. By harnessing the capabilities of a Large Vision-Language Model (LVLM), our framework effectively learns the deep visual-semantic correlations linking UAV and satellite imagery. Furthermore, we present a novel relational-aware loss function to optimize the training phase. By employing soft labels, this loss provides fine-grained supervision that avoids overly penalizing near-positive matches, ultimately boosting both the model's discriminative power and training stability. Comprehensive evaluations across various baseline architectures and standard benchmarks reveal that the proposed method substantially boosts the retrieval accuracy of existing models, yielding superior performance even under highly demanding conditions.