Determining the location of an image anywhere on Earth is a complex visual task, which makes it particularly relevant for evaluating computer vision algorithms. Yet, the absence of standard, large-scale, open-access datasets with reliably localizable images has limited its potential. To address this issue, we introduce OpenStreetView-5M, a large-scale, open-access dataset comprising over 5.1 million geo-referenced street view images, covering 225 countries and territories. In contrast to existing benchmarks, we enforce a strict train/test separation, allowing us to evaluate the relevance of learned geographical features beyond mere memorization. To demonstrate the utility of our dataset, we conduct an extensive benchmark of various state-of-the-art image encoders, spatial representations, and training strategies. All associated codes and models can be found at https://github.com/gastruc/osv5m.
@article{arxiv.2404.18873,
title = {OpenStreetView-5M: The Many Roads to Global Visual Geolocation},
author = {Guillaume Astruc and Nicolas Dufour and Ioannis Siglidis and Constantin Aronssohn and Nacim Bouia and Stephanie Fu and Romain Loiseau and Van Nguyen Nguyen and Charles Raude and Elliot Vincent and Lintao XU and Hongyu Zhou and Loic Landrieu},
journal= {arXiv preprint arXiv:2404.18873},
year = {2024}
}