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 Medium Bookcase 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 ORG SHIRITAKI SPAGHETTI W/OAT FIBRE 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.