OpenStreetView-5M: The Many Roads to Global Visual Geolocation
<|reference_start|>OpenStreetView-5M: The Many Roads to Global Visual Geolocation: 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.<|reference_end|>
@article{astruc2024openstreetview-5m:,
    title={OpenStreetView-5M: The Many Roads to Global Visual Geolocation},
    author={Guillaume Astruc, Nicolas Dufour, Ioannis Siglidis, Constantin
  Aronssohn, Nacim Bouia, Stephanie Fu, Romain Loiseau, Van Nguyen Nguyen,
  Charles Raude, Elliot Vincent, Lintao XU, Hongyu Zhou, Loic Landrieu},
    journal={arXiv preprint arXiv:2404.18873},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.18873},
    primaryClass={cs.CV cs.AI}
}