Rectified Embedding Flow Learning for Aerial Multi-view  Geo-localization

Aerial geo-localization is increasingly essential for large-scale spatial retrieval. To overcome the limitations of single-retrieval systems caused by modality-specific uncertainty in open environments, this paper introduces the unified Aerial Multi-view Geo-localization (AMGL) task. However, when applying universal multimodal retrieval paradigms to this task, the semantic bias introduced solely by textual instructions fails to resolve and reconstruct the differentiated distribution structures necessary for cross-view alignment. Consequently, this paper proposes the Rectified Embedding Flow Learning (REFL) framework, formulating crossdomain alignment as a directed conditional distribution transport process. Initially, REFL employs velocity-prior flow learning to fit continuous ordinary differential equation trajectories, deriving transformation priors that map single-view distributions to a latent shared manifold. Subsequently, a trajectory-guided embedding rectification mechanism continuously transports query features to the target view distribution, explicitly compensating for distribution shifts. Extensive evaluations on the Aerial MVGL benchmark demonstrate that REFL achieves state-of-theart performance, yielding an average R@1 of 44.83% and R@10 of 65.85%. The code and benchmark are available at https://github.com/rhaohur/REFL.