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).
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| Capetown, South Africa | London, UK | Melbourne, Australia | Mexico city, Mexico | |
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| New York, USA | Paris, France | Rio, Brazil | Taipei, China | |
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| Losangeles, USA | Manila, Philipine | Santiago, Chile | Sydney, Australia | |
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| Seattle, USA | Singapore | Barcelona, Span | Tokyo, Japan |
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| City scene | Nature scene | ||
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| Water scene | Occlusion | ||
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| London, UK | Rio, Brazil | Seattle, USA |
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| Singapore | Sydney, Australia | Taipei, China |
🚧 Under Construction 🛠️
python train/train_cvcities.pyThis code is based on the amazing work of:
🚧 Under Construction 🛠