Datasets:
File size: 1,782 Bytes
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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.*
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