City Typology

City typology research aims to classify and understand the diverse characteristics of urban environments, focusing on building structures, spatial layouts, and transportation systems. Current research employs advanced machine learning techniques, including graph neural networks, autoencoders, and diffusion models, to analyze large-scale datasets derived from satellite imagery, LiDAR point clouds, and volunteered geographic information. These models enable more accurate 3D city reconstruction, improved urban morphology analysis, and prediction of city characteristics like transportation typology, ultimately supporting urban planning, autonomous navigation, and other applications. The resulting advancements offer significant improvements in the precision and efficiency of urban modeling and analysis.