The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLVIII-1-2024
11 May 2024
 | 11 May 2024

Neural Cellular Automata-based Land Use Changes Simulation

Jinian Zhang and Lanfa Liu

Keywords: Convolutional Neural Networks, Land Use, Simulation, Neural Cellular Automata

Abstract. Simulating land use and land cover changes (LUCC) is important for urban planning and environmental studies. In this study, we introduce a neural cellular automata (NCA) model that integrates biological principles and convolutional neural networks (CNNs) for land use simulation. We conduct experiments in the city of Wuhan, China. The NCA model achieved the highest performance with an OA of 0.858, F1 score of 0.753, Kappa coefficient of 0.799, and FOM of 0.427. Comparisons of land use data of Wuhan city from 2000 and 2010 with the simulated optimal results indicate that forest areas closer to urban centers are more susceptible to modernization processes, showing the advantage of NCA in accurately simulating land use changes in the central urban area.