The Recipient acknowledges that the dataset is provided solely for non-commercial research and educational purposes, and shall not be used, directly or indirectly, for any commercial or revenue-generating purpose without prior written authorization from the data owners. By accessing the dataset, the Recipient agrees to comply with these restrictions.

UAV Satellite-Thermal Geo-localization Dataset

[2025/12] Update: We have released an updated version of satellite-thermal-dataset-v3, which excludes the test region from the generated data to ensure full alignment with the original paper’s evaluation protocol. If you wish to conduct a rigorous comparison with STHN, please replace extended_queries.h5 with extended_queries_test_excluded.h5.

Captured using Boson thermal cameras, 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:

Long-range UAV Thermal Geo-localization with Satellite Imagery
This study focuses on long-range geo-localization by leveraging UAV thermal imagery and satellite data through image retrieval techniques.
STHN: Deep Homography Estimation for UAV Thermal Geo-localization with Satellite Imagery
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:

For satellite-thermal-dataset-v1: https://arxiv.org/abs/2306.02994

@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}}

For satellite-thermal-dataset-v3: https://arxiv.org/abs/2405.20470

@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}}