A Spatial Analysis of Urban Streets under Deep Learning Based on Street View Imagery: Quantifying Perceptual and Elemental Perceptual Relationships

Haozun Sun, Hong Xu (), Hao He, Quanfeng Wei, Yuelin Yan, Zheng Chen, Xuanhe Li, Jialun Zheng and Tianyue Li
Additional contact information
Haozun Sun: School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China
Hong Xu: School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China
Hao He: State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China
Quanfeng Wei: School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China
Yuelin Yan: School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China
Zheng Chen: School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China
Xuanhe Li: School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China
Jialun Zheng: School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China
Tianyue Li: School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China

Sustainability, 2023, vol. 15, issue 20, 1-30

Abstract: Measuring the human perception of urban street space and exploring the street space elements that influence this perception have always interested geographic information and urban planning fields. However, most traditional efforts to investigate urban street perception are based on manual, usually time-consuming, inefficient, and subjective judgments. This shortcoming has a crucial impact on large-scale street spatial analyses. Fortunately, in recent years, deep learning models have gained robust element extraction capabilities for images and achieved very competitive results in semantic segmentation. In this paper, we propose a Street View imagery (SVI)-driven deep learning approach to automatically measure six perceptions of large-scale urban areas, including “safety”, “lively”, “beautiful”, “wealthy”, “depressing”, and “boring”. The model was trained on millions of people’s ratings of SVIs with a high accuracy. First, this paper maps the distribution of the six human perceptions of urban street spaces within the third ring road of Wuhan (appearing as Wuhan later). Secondly, we constructed a multiple linear regression model of “street constituents–human perception” by segmenting the common urban constituents from the SVIs. Finally, we analyzed various objects positively or negatively correlated with the six perceptual indicators based on the multiple linear regression model. The experiments elucidated the subtle weighting relationships between elements in different street spaces and the perceptual dimensions they affect, helping to identify the visual factors that may cause perceptions of an area to be involved. The findings suggested that motorized vehicles such as “cars” and “trucks” can negatively affect people’s perceptions of “safety”, which is different from previous studies. We also examined the influence of the relationships between perceptions, such as “safety” and “wealthy”. Finally, we discussed the “perceptual bias” issue in cities. The findings enhance the understanding of researchers and city managers of the psychological and cognitive processes behind human–street interactions.

Keywords: deep learning; street view images; urban perception; urban planning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/20/14798/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/20/14798/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:20:p:14798-:d:1258417

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().