Tensor‐CA: A high‐performance cellular automata model for land use simulation based on vectorization and GPU

Abstract With the ability to understand linkages and feedbacks between land use dynamics and human–land relationships, cellular automata (CA) are extensively applied in land use/cover change (LUCC) simulation. However, with complex transition rules and a growing volume of spatial data, conventional serial CA models cannot meet the demands of efficient computation. In this article, a Tensor‐CA model using vectorization and Graphics Processing Unit (GPU) technology based on a tensor computation framework for optimizing multiple LUCC simulations is presented. Complex transition rules of LUCC‐CA models are vectorized and formalized to tensor operations which are effectively solved by GPU. The proposed Tensor‐CA model was applied to LUCC simulations in the Pearl River Delta of China. The experimental results indicate that the proposed model effectively improved the performance compared to Serial‐CA, Parallel‐CA, and GPU‐CA.