Datasets:
File size: 1,832 Bytes
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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.*
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