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

VG-SSL: Benchmarking Self-supervised Representation Learning Approaches for Visual Geo-localization