A high‐performance cellular automata model for urban expansion simulation based on convolution and graphic processing unit

Abstract

Cellular automata (CA) models are effective tools for simulating future urban expansion. With the widespread use of high‐resolution geospatial data for CA simulation, the computational intensity of CA models has increased. Additionally, due to the continuous development of CA modeling research, many scholars have made improvements to the models to enhance their simulation accuracy, resulting in an increasing computational complexity of the model. Consequently, the simulation task based on CA requires vast computing time and memory space. In recent years, deep learning (DL) has experienced rapid development. Many open‐source DL frameworks support graphic processing unit (GPU) parallel computing and provide efficient application programming interfaces (APIs) that can be easily called to handle tasks of interest. In this study, a high‐performance CA model was constructed based on the similarity between the neighborhood effect calculation process of the CA model and the convolutional process in a convolutional neural network (CNN). The convolution function in the DL library is used to calculate the neighborhood effect of the CA model to reduce the time and memory consumption of CA‐based simulation. The experimental results show that compared with the conventional CA model, the execution time of the GPU‐convolution‐CA model proposed in this study has been reduced by more than 98%.