Advancing urban expansion modeling with a hybrid TRANSGAN deep learning approach

Abstract

Urban expansion modeling is pivotal for sustainable urban planning, yet conventional approaches often fail to capture intricate spatial and temporal dynamics. In this study, we present TRANSGAN, the first framework combining Transformer networks and Generative Adversarial Networks (GANs) for urban expansion simulation. By harnessing the spatial learning strengths of Transformers alongside the generative capabilities of GANs, TRANSGAN significantly outperforms traditional models, as evidenced by enhanced predictive accuracy and spatial consistency. Trained on historical land use data in Hong Kong and incorporating key drivers, such as proximity to CBDs, road networks, and elevation, the model delivers highly realistic urban expansion forecasts for 2035 and 2045. Comparative analyses with Transformer, GAN, U-Net, and Random Forest models demonstrate that TRANSGAN achieves the highest F1 Score (0.9496), Precision (0.9396), FOM (0.8889), and Recall (0.9428). This robust, interpretable, and scalable approach not only advances urban expansion modeling but also provides critical insights for urban planners and policymakers.