A Novel EAGLe Framework for Robust UAV-View Geo-Localization

This article addresses the unmanned aerial vehicle (UAV)-view geo-localization task, which focuses on bi-directional retrieval between UAV- and satellite-view images. Generally, existing methods aim to learn image representations that can distinguish between different locations while effectively mitigating the cross-view domain gap. However, these methods often struggle in noisy UAV flight environments, as they fail to account for environmental domain shifts caused by varying weather and lighting conditions. To this end, we propose a novel environment-agnostic geo-localization (EAGLe) framework, which integrates a dual-objective discriminator and a style mixture module into diverse UAV-view geo-localization networks to enhance their robustness in dynamic environments. Specifically, the dual-objective discriminator not only distinguishes between UAV and satellite views but also identifies various environmental styles in UAV-view images. Through adversarial learning, the dual-objective discriminator encourages the feature encoder to produce features that remain invariant to both viewpoint and environmental variations. Furthermore, the style mixture module is integrated into the feature encoder to extend diversity at the feature level, allowing EAGLe to learn a broader range of environmental styles beyond the training data. Extensive experiments on the University-1652 and SUES-200 datasets demonstrate that the proposed EAGLe significantly improves the reliability of UAV-view geo-localization networks under dynamic and unpredictable environmental conditions while maintaining inference efficiency.