太阳成8722(中国)有限公司-GREEN NO.1

标题: Measuring human perceptions of streetscapes to better inform urban renewal: A perspective of scene semantic parsing

作者: Ma, XY (Ma, Xiangyuan); Ma, CY (Ma, Chenyan); Wu, C (Wu, Chao); Xi, YL (Xi, Yuliang); Yang, RF (Yang, Renfei); Peng, NYZ (Peng, Ningyezi); Zhang, C (Zhang, Chen); Ren, F (Ren, Fu)

来源出版物: CITIES  卷: 110  文献号: 103086  DOI: 10.1016/j.cities.2020.103086  出版年: MAR 2021  

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.

入藏号: WOS:000618543800003

语言: English

文献类型: Article

作者关键词: Streetscape perception; Urban renewal; Visual trait; Semantic segmentation; Street-level imagery

地址: [Ma, Xiangyuan; Ma, Chenyan; Yang, Renfei; Zhang, Chen; Ren, Fu] Wuhan Univ, Sch Resources & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Peoples R China.

[Ma, Chenyan; Ren, Fu] Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan 430079, Peoples R China.

[Wu, Chao] Nanjing Univ Posts & Telecommun, Sch Geog & Biol Informat, Nanjing 210023, Peoples R China.

[Wu, Chao] Nanjing Univ Posts & Telecommun, Smart Hlth Big Data Anal & Locat Serv Engn Lab Ji, Nanjing 210023, Peoples R China.

[Xi, Yuliang] Anhui Univ, Sch Internet, Hefei 230039, Peoples R China.

[Peng, Ningyezi] Hong Kong Polytech Univ, Dept Land Surveying & Geoinformat, Hong Kong 999077, Peoples R China.

通讯作者地址: Ren, F (通讯作者)，Wuhan Univ, Sch Resources & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Peoples R China.

电子邮件地址: maxiangyuan@whu.edu.cn; chenyanma@sohu.com; chaowu@njupt.edu.cn; yuliangwh@163.com; youngfly@whu.edu.cn; ningyeziPeng@hotmail.com; czhang0315@whu.edu.cn; renfu@whu.edu.cn

影响因子：4.802