README.md

---
license: mit
size_categories:
- 10K<n<100K
task_categories:
- image-to-image
configs:
  - config_name: examples  # Name of the dataset subset, if applicable.
    data_files: "examples/*.jpg"
---
## UAV Satellite-Thermal Geo-localization Dataset

Captured using [Boson thermal cameras](https://www.flir.com/products/boson/?vertical=lwir&segment=oem), this dataset is specifically designed for research on nighttime UAV satellite-thermal geo-localization and satellite-thermal image translation. It has been utilized in the following works:

1. **[Long-range UAV Thermal Geo-localization with Satellite Imagery](https://github.com/arplaboratory/satellite-thermal-geo-localization)**  
   This study focuses on long-range geo-localization by leveraging UAV thermal imagery and satellite data through image retrieval techniques.

2. **[STHN: Deep Homography Estimation for UAV Thermal Geo-localization with Satellite Imagery](https://github.com/arplaboratory/STHN)**  
   The STHN model introduces deep homography estimation to enhance the accuracy of UAV thermal geo-localization using satellite imagery.

## Citation

If you use the dataset in your research, please consider citing the following publication:

1. For satellite-thermal-dataset-v1:

```
@INPROCEEDINGS{xiao2023stgl,
  author={Xiao, Jiuhong and Tortei, Daniel and Roura, Eloy and Loianno, Giuseppe},
  booktitle={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, 
  title={Long-Range UAV Thermal Geo-Localization with Satellite Imagery}, 
  year={2023},
  volume={},
  number={},
  pages={5820-5827},
  doi={10.1109/IROS55552.2023.10342068}}
```

2. For satellite-thermal-dataset-v3:
```
@ARTICLE{xiao2024sthn,
  author={Xiao, Jiuhong and Zhang, Ning and Tortei, Daniel and Loianno, Giuseppe},
  journal={IEEE Robotics and Automation Letters}, 
  title={STHN: Deep Homography Estimation for UAV Thermal Geo-Localization With Satellite Imagery}, 
  year={2024},
  volume={9},
  number={10},
  pages={8754-8761},
  keywords={Estimation;Location awareness;Satellites;Satellite images;Autonomous aerial vehicles;Accuracy;Iterative methods;Deep learning for visual perception;aerial systems: applications;localization},
  doi={10.1109/LRA.2024.3448129}}
```

## Copyright Notice for Bing Satellite Imagery

Please note that our dataset is based on Bing satellite imagery. For detailed copyright information, please refer to the [Bing Maps Print Rights](https://www.microsoft.com/en-us/maps/bing-maps/product/print-rights) page.