initiacms/XLRS-Bench_visual_grounding_zh

🐙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](https://captain-whu.github.io/DOTA) RGB images from Google Earth and CycloMedia (for academic use only; commercial use is prohibited, and Google Earth terms of use apply).

• —[ITCVD](https://phys-techsciences.datastations.nl/dataset.xhtml?persistentId=doi:10.17026/dans-xnc-h2fu) Licensed under CC-BY-NC-SA-4.0.

• —[MiniFrance](https://ieee-dataport.org/open-access/minifrance), [HRSCD](https://ieee-dataport.org/open-access/hrscd-high-resolution-semantic-change-detection-dataset) Released under IGN’s "licence ouverte".

• —[Toronto, Potsdam](https://www.isprs.org/education/benchmarks/UrbanSemLab/default.aspx): 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:
• —The data must not be used for other than research purposes. Any other use is prohibited.
• —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.
• —The data must not be distributed to third parties. Any person interested in the data may obtain them via ISPRS WG III/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 [wfx23@nudt.edu.cn](mailto:wfx23@nudt.edu.cn). 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}
}