🐙GitHub

Information or evaluatation on this dataset can be found in this repo: https://github.com/AI9Stars/XLRS-Bench

📜Dataset License

Annotations of this dataset is released under a Creative Commons Attribution-NonCommercial 4.0 International License. For images from:

• DOTA
  RGB images from Google Earth and CycloMedia (for academic use only; commercial use is prohibited, and Google Earth terms of use apply).

• ITCVD
  Licensed under CC-BY-NC-SA-4.0.

• MiniFrance, HRSCD
  Released under IGN’s "licence ouverte".

• Toronto, Potsdam:
  The Toronto test data images are derived from the Downtown Toronto dataset provided by Optech Inc., First Base Solutions Inc., GeoICT Lab at York University, and ISPRS WG III/4, and are subject to the following conditions:

  1. The data must not be used for other than research purposes. Any other use is prohibited.
  2. The data must not be used outside the context of this test project, in particular while the project is still on-going (i.e. until September 2012). Whether the data will be available for other research purposes after the end of this project is still under discussion.
  3. The data must not be distributed to third parties. Any person interested in the data may obtain them via ISPRS WG III/4.
  4. The data users should include the following acknowledgement in any publication resulting from the datasets: “The authors would like to acknowledge the provision of the Downtown Toronto data set by Optech Inc., First Base Solutions Inc., GeoICT Lab at York University, and ISPRS WG III/4.”

Disclaimer:
If any party believes their rights are infringed, please contact us immediately at [email protected]. We will promptly remove any infringing content.

📖Citation

If you find our work helpful, please consider citing:

@inproceedings{wang2025xlrs,
  title={Xlrs-bench: Could your multimodal llms understand extremely large ultra-high-resolution remote sensing imagery?},
  author={Wang, Fengxiang and Wang, Hongzhen and Guo, Zonghao and Wang, Di and Wang, Yulin and Chen, Mingshuo and Ma, Qiang and Lan, Long and Yang, Wenjing and Zhang, Jing and others},
  booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
  pages={14325--14336},
  year={2025}
}

@article{wang2025geollava,
  title={GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K Resolution},
  author={Wang, Fengxiang and Chen, Mingshuo and Li, Yueying and Wang, Di and Wang, Haotian and Guo, Zonghao and Wang, Zefan and Shan, Boqi and Lan, Long and Wang, Yulin and others},
  journal={arXiv preprint arXiv:2505.21375},
  year={2025}
}