文献类型：期刊文献

英文题名：Automatic Identification of Potential Renewal Areas in Urban Residential Districts Using Remote Sensing Data and GeoAI

作者：Li, Wenzhu[1];Wang, Hui[1];Wang, Xinyu[2];Tu, Tangqi[3]

机构：[1]East China Univ Sci & Technol, Sch Art Design & Media, Dept Landscape Planning & Design, Shanghai 200237, Peoples R China;[2]Tsinghua Univ, Sch Architecture, Beijing 100084, Peoples R China;[3]Ningbo Univ, Dept Geog & Spatial Informat Tech, Ningbo 315211, Peoples R China

年份：2025

卷号：18

起止页码：8523

外文期刊名：IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

收录：;EI(收录号：20251218060964);WOS:【SCI-EXPANDED(收录号:WOS:001457520200003)】；

基金：This work was supported in part by the National Key Research and Development Program of China under Grant 2023YFC3805400, in part by the National Natural Science Foundation of China under Grant 52408060, in part by the Fundamental Research Funds for the Central Universities under Grant JKZ02242201, in part by the East China University of Science and Technology Faculty Industry-Academia-Research Practice Program under Grant YZ0130518, and in part by the Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission, and Shanghai Summit Discipline in Design. (Corresponding authors: Wenzhu Li;

语种：英文

外文关键词：Buildings; Urban areas; Remote sensing; Layout; Green buildings; Earth; Semantic segmentation; Deep learning; Government; Feature extraction; multisource data; remote sensing; semantic segmentation; urban renewal

摘要：Urban renewal is crucial for fostering revitalization and sustainable development in cities. Accurate identification of renewal areas in urban residential districts is essential for implementing effective renewal strategies. However, existing studies struggle with the automatic large-scale spatial classification of renewal areas due to their inherent complexity. Moreover, complete demolition and reconstruction are not always conducive to long-term sustainability. This study proposes an automated framework to identify potential renewal areas in urban residential districts, using remote sensing data and Geospatial Artificial Intelligence, with Shanghai as a case study. The framework began by establishing classification rules to identify different renewal modes: retention, renewal, and demolition areas. The data labeling process was performed using the Segment Anything Model, followed by a comprehensive identification of different renewal modes through deep learning models, specifically DeepLabv3+. The results showed that 1) a total of 327.18 km(2) of retention areas, 130.76 km(2) of renewal areas, and 37.31 km(2) of demolition areas were automatically identified; 2) retention areas were evenly distributed across the city, while potential renewal areas were concentrated in the central urban districts, and demolition areas were primarily located in the suburban regions. This automatic identification framework significantly enriches the understanding of spatial complexity in urban systems and broadens the application of remote sensing technology in urban studies. The findings provide valuable insights for urban planners, highlighting priority regions for targeted renewal efforts.