Summary of Openstreetview-5m: the Many Roads to Global Visual Geolocation, by Guillaume Astruc et al.
OpenStreetView-5M: The Many Roads to Global Visual Geolocation
by 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
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel computer vision dataset, OpenStreetView-5M, is introduced to facilitate the evaluation of algorithms for locating images on a map. This large-scale dataset comprises over 5.1 million geo-referenced street view images from 225 countries and territories, with a strict train/test separation. The authors demonstrate the utility of their dataset by benchmarking various state-of-the-art image encoders, spatial representations, and training strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to find a specific photo on a map. This is a tough task for computers too! Until now, there hasn’t been a reliable way to test how well computer vision algorithms can do this. To fix that, scientists created OpenStreetView-5M, a massive dataset of street view images with exact locations. They tested many different approaches and shared the results. |
