Urban Sprawl Simulation Integrating Multiscale and Coordinate Attention
In response to the issues like scale effects and insufficient feature extraction of non-stationarity in land cover change simulation based on machine learning-driven cellular automata,an ASPP (Atrous Spa-tial Pyramid Pooling)-CRA (Coordinate Attention) Unet-CARS (Cellular Automata for Raster Spaces) coupled model was constructed. Using real urban land use change data from the Chengdu-Chongqing eco-nomic circle in 2012,2016,and 2020,two sets of experiments were designed to validate the model's per-formance. It was then applied to predict urban expansion patterns of 2024 and 2028. Model comparison re-sults demonstrated that the ASPP-CRAUnet-CARS model achieved Kappa value of 0.9123 and FoM value of 0.4142,outperforming RF-CMCNN-CA and UMCNN-CA model in Kappa by 0.0208 and 0.0342,re-spectively,and in FoM by 0.0306 and 0.0679,respectively. Ablation studies revealed that removing the ASPP and CRA modules resulted in decreased Kappa and FoM values. The study suggests that the ASPP-CRAUnet-CARS model,integrating the advantages of traditional cellular automata and deep learning mod-els,can effectively learn complex spatial features in urban development,improve the modeling of spatial non-stationarity,and enhance simulation accuracy.
万方数据
维普
