Cross-View Geo-Localization: A Survey

Cross-view image geo-localization seeks to identify the geospatial location where a query image (i.e., street view image) was captured by matching it to a database of geo-tagged reference images such as satellite or aerial images. This problem has garnered notable attention in the realm of computer vision, spurred by the widespread availability of copious geo-tagged datasets and the advancements in machine learning techniques. This paper provides a thorough survey of cutting-edge methodologies, techniques, and associated challenges that are integral to this domain, with a focus on feature-based and deep learning strategies. Feature-based methods capitalize on unique features to establish correspondences across disparate viewpoints, whereas deep learning-based methodologies deploy neural networks (convolutional or transformer-based) to embed view-invariant attributes. This work also investigates the multifaceted challenges encountered in cross-view geo-localization (CVGL), such as variations in viewpoints and illumination, and the occurrence of occlusions, and it elucidates innovative solutions that have been formulated to tackle these issues. Furthermore, we document the benchmark datasets and relevant evaluation metrics and also perform a comparative analysis of state-of-the-art techniques. Finally, we conclude the paper with a discussion on prospective avenues for future research and the burgeoning applications of CVGL in an intricately interconnected global landscape.