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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
 ---
+# Pine Wilt Segmentation
+
+This dataset provides high-resolution aerial RGB imagery of pine forests in Jinju, South Korea, captured in January 2023 for semantic segmentation tasks focused on pine wilt disease detection. Acquired at 5-cm resolution using Korea Aerospace Research Institute (KARI) aerial platforms, the images depict field conditions with varying disease severity across pine tree canopies. The real-world, high-detail aerial data offers a practical resource for developing computer vision models in agricultural disease monitoring. The dataset contains 2,238 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{ha2025enhanced,
+  title={Enhanced pine wilt disease outbreak prediction: Integrating deep learning- detected infected trees with species distribution modeling},
+  author={Ha, Uirin and Kim, Hyungho and Kim, Seunguk and Choe, Hyeyeong},
+  journal={Ecological Informatics},
+  volume={91},
+  pages={103421},
+  year={2025},
+  publisher={Elsevier}
+}
+```
+
+
+*This dataset was reformatted from its original format to match HuggingFace standards.*