Datasets:
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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.* |