Duplicate from initiacms/XLRS-Bench-lite_VLM

Co-authored-by: Chen Mingshuo <initiacms@users.noreply.huggingface.co>

.gitattributes

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+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
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+*.jpg filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: cc-by-nc-sa-4.0
+task_categories:
+- visual-question-answering
+- multiple-choice
+- question-answering
+language:
+- en
+tags:
+- Remote Sensing
+size_categories:
+- 1K<n<10K
+---
+# 🐙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](http://creativecommons.org/licenses/by-nc-sa/4.0). 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](http://creativecommons.org/licenses/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"](https://web.archive.org/web/20200717042533/http://www.ign.fr/institut/activites/lign-lopen-data).
+
+- **[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:
+  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 **[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:
+
+```tex
+@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}
+}
+```

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