打印

作者

Xiangyuan Ma;Chenyan Ma;Chao Wu;Yuliang Xi;Renfei Yang;Ningyezi Peng;Chen Zhang;Fu Ren

来源

CITIES,Vol.110,Issue1,Article 103086

语言

英文

关键字

Streetscape perception;Urban renewal;Visual trait;Semantic segmentation;Street-level imagery

作者单位

School of Resources and Environmental Science, Wuhan University, Wuhan 430079, China;Key Laboratory of Geographic Information Systems, Ministry of Education, Wuhan University, Wuhan 430079, China;School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;School of Internet, Anhui University, Hefei 230039, China;Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, 999077, Hong Kong, China;School of Resources and Environmental Science, Wuhan University, Wuhan 430079, China;Key Laboratory of Geographic Information Systems, Ministry of Education, Wuhan University, Wuhan 430079, China;School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;School of Internet, Anhui University, Hefei 230039, China;Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, 999077, Hong Kong, China

摘要

Ubiquitous and up-to-date geotagged data are increasingly employed to uncover the visual traits of the built environment. However, few prior studies currently link this theoretical knowledge of street appraisals with operable practices to inform streetscape transformation. This study proposes a proof-of-concept analytical framework that sheds light on the connections between urban renewal and the quantification of streetscape visual traits. By virtue of a million intensively collected panoramic street view images in Shenzhen, China, the image-segmentation technique SegNet automatically extracts pixelwise semantical information and classifies visual elements. The throughput of the eye-level perception of the street canyon is formed by five indices. Additionally, the framework-derived scores (FDSs) are contrasted with the subjective rating scores (SRSs) to report the divergence and coherence between the visually experienced and the quantitative estimated methods. Furthermore, we investigate the spatial heterogeneity of five perception aspects, discuss the variations of the perception outcomes across different function streets, and analyze the net effect of urban renewal projects (URPs) on streetscape transformation. We conclude that this deep learning-driven approach provides a feasible paradigm to depict high-resolution streetscape perception, to analyze fine-scale built environment, and to effectively bridge gaps between the street semantic metric and urban renewal.