Extracting Meso- and Microscale Patterns of Urban Morphology Evolution: Evidence from Binhai New Area of Tianjin, China

Xiaojin Huang, Ran Cheng, Jun Wu (), Wenjian Yang, Longhao Zhang, Pengbo Li and Wenzhe Zhu
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Xiaojin Huang: School of Architecture, Tianjin Chengjian University, Tianjin 300380, China
Ran Cheng: School of Architecture, Tianjin Chengjian University, Tianjin 300380, China
Jun Wu: School of Architecture, Tianjin Chengjian University, Tianjin 300380, China
Wenjian Yang: International School of Engineering, Tianjin Chengjian University, Tianjin 300380, China
Longhao Zhang: School of Architecture, Tianjin University, Tianjin 300072, China
Pengbo Li: School of Architecture, Tianjin Chengjian University, Tianjin 300380, China
Wenzhe Zhu: School of Architecture, Tianjin Chengjian University, Tianjin 300380, China

Land, 2024, vol. 13, issue 11, 1-27

Abstract: Understanding and recognizing urban morphology evolution is a crucial issue in urban planning, with extensive research dedicated to detecting the extent of urban expansion. However, as urban development patterns shift from incremental expansion to stock optimization, related studies on meso- and microscale urban morphology evolution face limitations such as insufficient spatiotemporal data granularity, poor generalizability, and inability to extract internal evolution patterns. This study employs deep learning and meso-/microscopic urban form indicators to develop a generic framework for extracting and describing the evolution of meso-/microscale urban morphology. The framework includes three steps: constructing specific urban morphology datasets, semantic segmentation to extract urban form, and mapping urban form evolution using the Tile-based Urban Change (TUC) classification system. We applied this framework to conduct a combined quantitative and qualitative analysis of the internal urban morphology evolution of Binhai New Area from 2009 to 2022, with detailed visualizations of morphology evolution at each time point. The study identified that different locations in the area exhibited seven distinct evolution patterns: edge areal expansion, preservation of developmental potential, industrial land development pattern, rapid comprehensive demolition and construction pattern, linear development pattern, mixed evolution, and stable evolution. The results indicate that in the stock development phase, high-density urban areas exhibit multidimensional development characteristics by region, period, and function. Our work demonstrates the potential of using deep learning and grid classification indicators to study meso-/microscale urban morphology evolution, providing a scalable, cost-effective, quantitative, and portable approach for historical urban morphology understanding.

Keywords: urban morphology evolution; meso- and microscale; deep learning; visualization; urban planning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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