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

Author

Listed:
  • 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
    Hubei Provincial Engineering Research Center of Urban Regeneration, 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)

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.

Suggested Citation

  • Haozun Sun & Hong Xu & Hao He & Quanfeng Wei & Yuelin Yan & Zheng Chen & Xuanhe Li & Jialun Zheng & Tianyue Li, 2023. "A Spatial Analysis of Urban Streets under Deep Learning Based on Street View Imagery: Quantifying Perceptual and Elemental Perceptual Relationships," Sustainability, MDPI, vol. 15(20), pages 1-30, October.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:14798-:d:1258417
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    References listed on IDEAS

    as
    1. Yu Ye & Wei Zeng & Qiaomu Shen & Xiaohu Zhang & Yi Lu, 2019. "The visual quality of streets: A human-centred continuous measurement based on machine learning algorithms and street view images," Environment and Planning B, , vol. 46(8), pages 1439-1457, October.
    2. Mei Liu & Ying Jiang & Junliang He, 2021. "Quantitative Evaluation on Street Vitality: A Case Study of Zhoujiadu Community in Shanghai," Sustainability, MDPI, vol. 13(6), pages 1-21, March.
    3. Kanyou Sou & Hiroya Shiokawa & Kento Yoh & Kenji Doi, 2021. "Street Design for Hedonistic Sustainability through AI and Human Co-Operative Evaluation," Sustainability, MDPI, vol. 13(16), pages 1-22, August.
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    Cited by:

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    2. Ibrahim Mutambik, 2024. "Assessing Urban Vulnerability to Emergencies: A Spatiotemporal Approach Using K-Means Clustering," Land, MDPI, vol. 13(11), pages 1-22, October.
    3. Minglu Qin & Haibin Xu & Jiantuan Huang, 2024. "Investigating the Impact of Streetscape and Land Surface Temperature on Cycling Behavior," Sustainability, MDPI, vol. 16(5), pages 1-14, February.

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