VG-SSL: Benchmarking Self-Supervised Representation Learning Approaches for Visual Geo-Localization

Visual Geo-localization (VG) is a critical research area for identifying geo-locations from visual inputs particularly in autonomous navigation for robotics and vehicles. Current VG methods often learn feature extractors from geo-labeled images to create dense geographically relevant representations. Recent advances in Self-Supervised Learning (SSL) have demonstrated its capability to achieve performance on par with supervised techniques with unlabeled images. This study presents a novel VG-SSL framework designed for versatile integration and benchmarking of diverse SSL methods for representation learning in VG featuring a unique geo-related pair strategy GeoPair. Through extensive performance analysis we adapt SSL techniques to improve VG on datasets from hand-held and car-mounted cameras used in robotics and autonomous vehicles. Our results show that contrastive learning and information maximization methods yield superior geo-specific representation quality matching or surpassing the performance of state-of-the-art VG techniques. To our knowledge This is the first benchmarking study of SSL in VG highlighting its potential in enhancing geo-specific visual representations for robotics and autonomous vehicles. The code is publicly available at https://github.com/arplaboratory/VG-SSL.