Cross-view geo-localization（CVGL） is beset with numerous difficulties and challenges, mainly due to the significant discrepancies in viewpoint, the intricacy of localization scenarios, and global localization needs. Given these challenges, we present a novel cross-view image geo-localization framework. The experimental results demonstrate that the proposed framework outperforms existing methods on multiple public datasets and self-built datasets. To improve the cross-view geo-localization performance of the framework on a global scale, we have built a novel global cross-view geo-localization dataset, CV-Cities. This dataset encompassing a diverse range of intricate scenarios. It serves as a challenging benchmark for cross-view geo-localization.
We collected 223,736 ground images and 223,736 satellite images with high-precision GPS coordinates of 16 typical cities in five continents. To download this dataset, you can click: 🤗CV-Cities or 🤗CV-Cities (mirror).
 |
 |
 |
 |
| Capetown, South Africa | London, UK | Melbourne, Australia | Mexico city, Mexico |
 |
 |
 |
 |
| New York, USA | Paris, France | Rio, Brazil | Taipei, China |
 |
 |
 |
 |
| City scene | Nature scene | ||
 |
 |
 |
 |
| Water area | Occlusion | ||
 |
 |
Other scenes... | |
| Season Change | |||
 |
 |
 |
 |
 |
| London, UK | Rio, Brazil | Seattle, USA |
 |
 |
 |
| Singapore | Sydney, Australia | Taipei, China |
🚧 Under Construction 🛠️
python train/train_cvcities.pyThis code is based on the amazing work of:
🚧 Under Construction 🛠