| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: mask |
| dtype: image |
| splits: |
| - name: train |
| num_bytes: 16263869 |
| num_examples: 2042 |
| download_size: 17997550 |
| dataset_size: 16263869 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| license: apache-2.0 |
| task_categories: |
| - image-segmentation |
| size_categories: |
| - 1K<n<10K |
| --- |
| # Rural Lucc Segmentation |
|
|
| This dataset provides RGB satellite imagery for semantic segmentation of rural land use and land cover change in Jiangning District, Nanjing City, China. Captured in 2024 from a satellite platform, the images depict field environments typical of agricultural landscapes under real-world conditions. The real-world data offers a valuable resource for computer vision research focused on rural land monitoring and analysis. The dataset contains 2,042 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{zhao2025land, |
| title={Land-Unet: A deep learning network for precise segmentation and identification of non-structured land use types in rural areas for green urban space analysis}, |
| author={Zhao, Yan and Xie, Junru and Zhu, Huiru and Luo, Taige and Xiong, Yao and Fan, Chenyang and Xia, Haoxiang and Chen, Yuheng and Zhang, Fuquan}, |
| journal={Ecological Informatics}, |
| volume={87}, |
| pages={103078}, |
| year={2025}, |
| publisher={Elsevier} |
| } |
| ``` |
|
|
| https://www.kaggle.com/datasets/vvghigh/ruraluse |
|
|
| *This dataset was reformatted from its original format to match HuggingFace standards.* |
