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

Abstract

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.