  Cross-view geo-localization has garnered notable attention in the realm of
computer vision, spurred by the widespread availability of copious geotagged
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 convolutional neural networks
to embed view-invariant attributes. This work also delineates the multifaceted
challenges encountered in cross-view geo-localization, such as variations in
viewpoints and illumination, the occurrence of occlusions, and it elucidates
innovative solutions that have been formulated to tackle these issues.
Furthermore, we delineate 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 cross-view geo-localization
in an intricately interconnected global landscape.
