| --- |
| dataset_info: |
| features: |
| - name: bands |
| dtype: binary |
| - name: bands_shape |
| list: int64 |
| - name: bands_dtype |
| dtype: string |
| - name: band_metadata |
| dtype: string |
| - name: band_order |
| list: string |
| - name: mask |
| dtype: image |
| - name: rgb |
| dtype: image |
| - name: split |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 351984049 |
| num_examples: 595 |
| download_size: 328624419 |
| dataset_size: 351984049 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| license: cc-by-4.0 |
| task_categories: |
| - image-segmentation |
| size_categories: |
| - n<1K |
| --- |
| # Tree Crown Segmentation |
|
|
| This dataset provides real multispectral imagery of Camellia oleifera tree crowns collected in a field environment in China. Captured using a UAV platform with a DJI Mavic 3 M drone featuring RGB and multispectral sensors, it offers high-resolution aerial data for semantic segmentation applications in agricultural monitoring. The dataset contains 595 images with pixel-level mask annotations. |
|
|
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. |
|
|
| The original train/test/val split has been preserved in the `split` column. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{peng2026uav, |
| title={UAV-based two-stage deep learning for tree crown segmentation and height estimation in Camellia oleifera plantations}, |
| author={Peng, Yongkang and Yan, Enping and Xu, Xiaocheng and Mo, Dengkui and Wei, Wei}, |
| journal={Ecological Informatics}, |
| volume={96}, |
| pages={103868}, |
| year={2026}, |
| publisher={Elsevier} |
| } |
| ``` |
|
|
| peng, . yongkang . (2026). experimental data [Figure]. Zenodo. https://doi.org/10.5281/zenodo.18505981 |
|
|
|
|
| *This dataset was reformatted from its original format to match HuggingFace standards.* |
|
|
