  Cross-view geo-localization in GNSS-denied environments aims to determine an
unknown location by matching drone-view images with the correct geo-tagged
satellite-view images from a large gallery. Recent research shows that learning
discriminative image representations under specific weather conditions can
significantly enhance performance. However, the frequent occurrence of unseen
extreme weather conditions hinders progress. This paper introduces MCGF, a
Multi-weather Cross-view Geo-localization Framework designed to dynamically
adapt to unseen weather conditions. MCGF establishes a joint optimization
between image restoration and geo-localization using denoising diffusion
models. For image restoration, MCGF incorporates a shared encoder and a
lightweight restoration module to help the backbone eliminate weather-specific
information. For geo-localization, MCGF uses EVA-02 as a backbone for feature
extraction, with cross-entropy loss for training and cosine distance for
testing. Extensive experiments on University160k-WX demonstrate that MCGF
achieves competitive results for geo-localization in varying weather
conditions.
