Xu et al. (2025) Dataset for ‘UCL-CA: A Deep Learning-driven Sub-pixel Cellular Automaton for Land Use/Land Cover Change Simulation’

Identification

• Journal: Mendeley Data
• Year: 2025
• Date: 2025-11-19
• Authors: Xu, Qiuyi, Tang, Xuguang
• DOI: 10.17632/txfd87j8tr.1

Research Groups

Qiuyi Xu, Xuguang Tang

Short Summary

This paper introduces UCL-CA, a novel deep learning-driven sub-pixel cellular automaton, to simulate land use/land cover changes with enhanced spatial detail.

Objective

• To develop and evaluate UCL-CA, a deep learning-driven sub-pixel cellular automaton, for simulating land use/land cover changes at a finer spatial resolution than traditional pixel-based models.

Study Configuration

• Spatial Scale: Sub-pixel level, implying a resolution finer than the original pixel resolution of input data, but specific dimensions are not provided.
• Temporal Scale: Not explicitly stated, but involves simulating "change," suggesting a multi-temporal analysis or prediction over a period.

Methodology and Data

• Models used: UCL-CA (a Deep Learning-driven Sub-pixel Cellular Automaton).
• Data sources: Remote sensing data for land use/land cover information.

Main Results

• The UCL-CA model successfully integrates deep learning with sub-pixel cellular automata to provide a more detailed and accurate simulation of land use/land cover changes.

Contributions

• The primary contribution is the development of UCL-CA, a novel framework that combines deep learning with sub-pixel cellular automata, offering an advanced approach for simulating land use/land cover changes with improved spatial detail and potentially higher accuracy compared to existing pixel-based or non-deep learning models.

Funding

• Not specified in the provided text.

Citation

@article{Xu2025Dataset,
  author = {Xu, Qiuyi and Tang, Xuguang},
  title = {Dataset for ‘UCL-CA: A Deep Learning-driven Sub-pixel Cellular Automaton for Land Use/Land Cover Change Simulation’},
  journal = {Mendeley Data},
  year = {2025},
  doi = {10.17632/txfd87j8tr.1},
  url = {https://doi.org/10.17632/txfd87j8tr.1}
}