GT-LandSDS: A Novel Spatiotemporal Integrated Framework for Land Use Simulation by Coupling Cellular Automata with Graph Attention Network and Transformer

To address the limitation of traditional cellular automata models in effectively integrating temporal and spatial information, this study extends the previously developed Land use Simulation and Decision-Support system (LandSDS). By incorporating a graph attention network (GAT), a transformer, and an agent-based model (ABM) into a cellular automata framework informed by remote sensing time series, GT-LandSDS is constructed. Specifically, GAT dynamically captures higher-order spatial dependencies among land parcels; the self-attention mechanism of the transformer extracts land use change characteristics from multi-period observations; and ABM captures human behavioral decisions of three types, namely traffic, resident, and government. Based on this framework, GT-LandSDS derives CA transition rules from multiple dimensions and enhances the dynamic exploration of land use change across space and time. Using Guangxi Zhuang Autonomous Region as a case study, the model was validated with remote sensing land use data from six periods (2000, 2005, 2010, 2015, 2020, and 2023), and scenario-based future predictions were generated. The results show that: (1) The overall accuracy reaches 0.926, while the Kappa coefficient is 0.820, and the figure of merit (FoM) for change simulation is 0.034, indicating a relative advantage over ANN-CA, LSTM-CA, and UESP in overall pattern simulation, although fine-scale change reproduction remains limited; (2) Three development scenarios were then assessed: continuing historical trends, theoretical high-intensity urban expansion, and karst landform conservation under a green transformation development policy. The land use pattern of the area from 2023 to 2035 was predicted. The findings reveal that accelerating urbanization leads to rapid expansion of construction land, increasing by more than 88% compared with 2023, and causes substantial cropland loss. In contrast, intervention through the green transformation development policy limits construction land growth to 26.5%, effectively curbing urban sprawl while protecting forest, grassland, and cropland resources in the karst landscape. This study offers new insights into land use change simulation in ecologically fragile regions subject to strong policy interventions. It provides a scientific basis for coordinating ecological conservation and high-quality development in karst areas.