Cross-View Image Geolocalization: Challenges, Solutions, and Future Directions

Finally, this review provides an outlook on the application areas and future development directions of cross-view image geolocalization. Despite significant breakthroughs and progress in current cross-view geolocalization methods, several challenges and issues remain. These include high CPU memory requirements, limited availability of multi-modal datasets, and the difficulty in distinguishing ground objects in similar scenes. To address these challenges and advance the field, several potential solutions and future research priorities are proposed. These include enhancing processing capabilities for large-scale data, creating multi-modal geolocalization datasets, integrating sensor data from different sources, and enabling matching and adaptive geolocalization in non-rigid scenarios. These efforts aim to drive further development and innovation in cross-view image geolocalization.