Datasets:
File size: 1,881 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
splits:
- name: train
num_bytes: 3046469158
num_examples: 11336
download_size: 2325883850
dataset_size: 3046469158
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
task_categories:
- image-segmentation
size_categories:
- 10K<n<100K
---
# Subalpine Forest Segmentation
This dataset provides real RGB imagery of subalpine forest ecosystems captured in the field using a DJI Mavic 2 UAV equipped with a 1-inch CMOS camera. Collected on June 15, 2020, in Wanglang National Nature Reserve, China, the images depict natural forest structures and ground cover under typical high-altitude temperate conditions. It serves as a field-based resource for semantic segmentation tasks in ecological monitoring and forest analysis. The dataset contains 11,336 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{shi2025evaluation,
title={Evaluation of a CNN model to map vegetation classification in a subalpine coniferous forest using UAV imagery},
author={Shi, Weibo and Liao, Xiaohan and Wang, Shaoqiang and Ye, Huping and Wang, Dongliang and Yue, Huanyin and Liu, Jianli},
journal={Ecological Informatics},
volume={87},
pages={103111},
year={2025},
publisher={Elsevier}
}
```
Shi, weibo (2025), “CNN model to map vegetation classification in a subalpine coniferous forest using UAV imagery”, Mendeley Data, V2, doi: 10.17632/d9f4m2735b.2
*This dataset was reformatted from its original format to match HuggingFace standards.*
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