A novel spatiotemporal urban land change simulation model: Coupling transformer encoder, convolutional neural network, and cellular automata.

Land use and land cover change (LUCC) process exhibits spatial correlation and temporal dependency. Accurate extraction of spatiotemporal features is important in enhancing the modeling capabilities of LUCC. Cellular automaton (CA) models, recognized as powerful tools for simulating dynamic LUCC processes, are traditionally applied in LUCC, focusing on time-slice driving factor data, often neglecting the temporal dimension. However, the transformer architecture, a highly acclaimed model in machine learning, has been rarely integrated into CA models for the simulation of dynamic LUCC processes. To fill this gap, we proposed a novel spatiotemporal urban LUCC simulation model, namely, transformer-convolutional neural network (TC)-CA. Based on CA models that involve the utilization of a convolutional neural network (CNN) for extracting latent spatial features, TC-CA extends this paradigm by incorporating a transformer architecture to extract spatiotemporal information from temporal driving factor data and temporal spatial features. The evaluation results with Wuxi city as a study area indicated the advantage of our proposed TC-CA against random forest-CA, conventional CNN-CA, artificial neural network-CA, and transformer-CA. Compared with the three non-transformer-based CAs, the TC-CA improved the figure of merit by up to 2.85%–8.14%. This study contributes a fresh spatiotemporal perspective and transformer approach to the field of LUCC modeling. [ABSTRACT FROM AUTHOR]