Pix2Pix-Based Modelling of Urban Morphogenesis and Its Linkage to Local Climate Zones and Urban Heat Islands in Chinese Megacities

Mo Wang, Ziheng Xiong, Jiayu Zhao, Shiqi Zhou () and Qingchan Wang ()
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Mo Wang: College of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China
Ziheng Xiong: College of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China
Jiayu Zhao: College of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China
Shiqi Zhou: College of Design and Innovation, Tongji University, Shanghai 200093, China
Qingchan Wang: Art School, Hunan University of Information Technology, Changsha 410151, China

Land, 2025, vol. 14, issue 4, 1-21

Abstract: Accelerated urbanization in China poses significant challenges for developing urban planning strategies that are responsive to diverse climatic conditions. This demands a sophisticated understanding of the complex interactions between 3D urban forms and local climate dynamics. This study employed the Conditional Generative Adversarial Network (cGAN) of the Pix2Pix algorithm as a predictive model to simulate 3D urban morphologies aligned with Local Climate Zone (LCZ) classifications. The research framework comprises four key components: (1) acquisition of LCZ maps and urban form samples from selected Chinese megacities for training, utilizing datasets such as the World Cover database, RiverMap’s building outlines, and integrated satellite data from Landsat 8, Sentinel-1, and Sentinel-2; (2) evaluation of the Pix2Pix algorithm’s performance in simulating urban environments; (3) generation of 3D urban models to demonstrate the model’s capability for automated urban morphology construction, with specific potential for examining urban heat island effects; (4) examination of the model’s adaptability in urban planning contexts in projecting urban morphological transformations. By integrating urban morphological inputs from eight representative Chinese metropolises, the model’s efficacy was assessed both qualitatively and quantitatively, achieving an RMSE of 0.187, an R 2 of 0.78, and a PSNR of 14.592. In a generalized test of urban morphology prediction through LCZ classification, exemplified by the case of Zhuhai, results indicated the model’s effectiveness in categorizing LCZ types. In conclusion, the integration of urban morphological data from eight representative Chinese metropolises further confirmed the model’s potential in climate-adaptive urban planning. The findings of this study underscore the potential of generative algorithms based on LCZ types in accurately forecasting urban morphological development, thereby making significant contributions to sustainable and climate-responsive urban planning.

Keywords: climate-responsive urban planning; deep learning; Landsat; Sentinel (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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