| --- |
| 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.* |
|
|
