---
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.*