| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: mask |
| dtype: image |
| splits: |
| - name: train |
| num_bytes: 1903512865 |
| num_examples: 2238 |
| download_size: 1907953672 |
| dataset_size: 1903512865 |
| configs: |
| - config_name: default |
| 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} |
| } |
| ``` |
|
|
| Ha, Uirin; Kim, Hyungho; Kim, Seunguk; Choe, Hyeyeong (2025), “Enhanced pine wilt disease outbreak prediction: integrating deep learning-detected infected trees with species distribution modeling”, Mendeley Data, V3, doi: 10.17632/swpp8jxymv.3 |
|
|
| *This dataset was reformatted from its original format to match HuggingFace standards.* |
|
|
