Fog-Robust Cross-View Geo-Localization: Datasets and Framework

Cross-view geo-localization (CVGL) aims to match ground-view queries with satellite images, yet existing works largely assume clear visibility and rarely consider adverse weather conditions such as fog. Fog usually obscures textures and structural cues essential for cross-view correspondence. To enable controlled investigation, we construct two synthetic foggy CVGL datasets, CVUSA-Fog and CVACT-Fog, using the atmospheric scattering model while preserving the original ground–satellite correspondence. Beyond synthetic evaluation, we manually curate 1,441 real-fog ground-view images and pair them with satellite images to construct CVFog. Additionally, we further propose a plug-and-play Wavelet-guided Attenuation Structural Adapter (WASA) module, which couples low-frequency degradation cues with structural responses to selectively calibrate fog-affected yet informative regions in ground-view features. Experiments across multiple backbones, datasets, and fog intensities show that synthetic-fog training substantially improves both fog-domain retrieval and synthetic-to-real performance on CVFog, while WASA provides consistent further gains. We further study semi-supervised real-fog adaptation with limited pairing supervision. Under the same real-fog pairing budget, confidence-filtered hard pseudo-pairs improve retrieval over supervised adaptation by exploiting pair-unlabeled CVFog images.