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README.md

@@ -16,4 +16,31 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+license: cc-by-4.0
+task_categories:
+- image-segmentation
+size_categories:
+- 1K<n<10K
 ---
+# Rural Lucc Segmentation
+
+This dataset provides RGB satellite imagery for semantic segmentation of rural land use and land cover change in Jiangning District, Nanjing City, China. Captured in 2024 from a satellite platform, the images depict field environments typical of agricultural landscapes under real-world conditions. The real-world data offers a valuable resource for computer vision research focused on rural land monitoring and analysis. The dataset contains 2,042 images with pixel-level mask annotations.
+
+This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
+
+## Citation
+
+```bibtex
+@article{zhao2025land,
+  title={Land-Unet: A deep learning network for precise segmentation and identification of non-structured land use types in rural areas for green urban space analysis},
+  author={Zhao, Yan and Xie, Junru and Zhu, Huiru and Luo, Taige and Xiong, Yao and Fan, Chenyang and Xia, Haoxiang and Chen, Yuheng and Zhang, Fuquan},
+  journal={Ecological Informatics},
+  volume={87},
+  pages={103078},
+  year={2025},
+  publisher={Elsevier}
+}
+```
+
+
+*This dataset was reformatted from its original format to match HuggingFace standards.*