Mapping the Vanishing and Transformation of Urban Villages in China
Wenyu Zhang, Yao Tong, Yiqiu Liu, Rui Cao

TL;DR
This study employs deep learning to monitor and analyze the complex, nonlinear transformation of urban villages in China, revealing patterns of redevelopment and land use changes across four major cities.
Contribution
It introduces a novel deep learning framework for systematic spatiotemporal mapping of urban village transformations using remote sensing imagery.
Findings
Redevelopment processes are often prolonged.
Urban cores tend to be more stable than peripheral areas.
Three main transformation pathways identified: synchronized, delayed, and gradual redevelopment.
Abstract
Urban villages (UVs), informal settlements embedded within China's urban fabric, have undergone widespread demolition and redevelopment in recent decades. However, there remains a lack of systematic evaluation of whether the demolished land has been effectively reused, raising concerns about the efficacy and sustainability of current redevelopment practices. To address the gap, this study proposes a deep learning-based framework to monitor the spatiotemporal changes of UVs in China. Specifically, semantic segmentation of multi-temporal remote sensing imagery is first used to map evolving UV boundaries, and then post-demolition land use is classified into six categories based on the "remained-demolished-redeveloped" phase: incomplete demolition, vacant land, construction sites, buildings, green spaces, and others. Four representative cities from China's four economic regions were…
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Taxonomy
TopicsUrbanization and City Planning · Urban Planning and Governance · China's Socioeconomic Reforms and Governance
