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A Spatial Visual Quality Evaluation Method for an Urban Commercial Pedestrian Street Based on Streetscape Images—Taking Tianjin Binjiang Road as an Example

Author

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  • Xiaofei Li

    (School of Landscape Architecture, Northeast Forestry University, Harbin 150040, China)

  • Chunyu Pang

    (School of Landscape Architecture, Northeast Forestry University, Harbin 150040, China)

Abstract

As core public spaces in cities, urban commercial pedestrian streets are important destinations for local residents and foreign tourists, and confusion regarding the visual space of a commercial pedestrian street sends direct environmental warning signals to pedestrians, affecting their visiting decisions. In this paper, through an investigation consisting of the artificial field simulation of the visual perception of pedestrians, we collect the corresponding street images and extract visual elements using the full convolutional network. Semantic segmentation is performed to obtain the visual parameters of the street. According to the quantitative model, the visual elements are matched with geographic elements, and a geographic information database is established to evaluate the spatial visual quality of commercial pedestrian streets. (1) There is obvious spatial heterogeneity in the spatial visual quality of different streets in commercial pedestrian streets. (2) The building heights, street widths, as well as the street vegetation, facilities, and landscape vignettes are spatial elements that shape the spatial visual quality of commercial pedestrian streets. (3) The main distribution of commercial facilities and the distribution of active businesses have an important impact on the degree of crowd gathering in a street space and the visual spatial quality of a street. This paper provides comparable data collection methods and research methods for the visual spatial quality of commercial pedestrian streets. This paper can also provide valuable data for the design, planning, and sustainable renewal management and regulation of the visual perception of commercial pedestrian streets.

Suggested Citation

  • Xiaofei Li & Chunyu Pang, 2024. "A Spatial Visual Quality Evaluation Method for an Urban Commercial Pedestrian Street Based on Streetscape Images—Taking Tianjin Binjiang Road as an Example," Sustainability, MDPI, vol. 16(3), pages 1-20, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:3:p:1139-:d:1329009
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    References listed on IDEAS

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    1. Bas Spierings, 2023. "Leisure mobilities, shopping routes and sensescapes: youth in the city centre of Utrecht," Mobilities, Taylor & Francis Journals, vol. 18(5), pages 719-739, September.
    2. Ye Sun & Wei Lu & Zongchao Gu, 2023. "Analysis of spatial form and structure of commercial pedestrian blocks based on Isovist and big data," Environment and Planning B, , vol. 50(5), pages 1313-1327, June.
    3. 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.
    4. Jaewon Han & Sugie Lee, 2023. "Verification of Immersive Virtual Reality as a Streetscape Evaluation Method in Urban Residential Areas," Land, MDPI, vol. 12(2), pages 1-16, January.
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    Cited by:

    1. Jizhou Chen & Xiaobin Li & Jialing Chen & Lijun Xu & Hao Feng & Rong Zhu, 2025. "Identifying and Prioritising Public Space Demands in Historic Districts: Perspectives from Tourists and Local Residents in Yangzhou," Land, MDPI, vol. 14(9), pages 1-49, September.
    2. Caijian Hua & Wei Lv, 2025. "Optimizing Semantic Segmentation of Street Views with SP-UNet for Comprehensive Street Quality Evaluation," Sustainability, MDPI, vol. 17(3), pages 1-20, February.
    3. Jiayu Xu & Yuxuan Liu & Jingfen Wu & Xuan Wang & Yu Ye, 2025. "Exploring Heterogeneous and Non-Linear Effects of the Built Environment on Street Quality: A Computational Approach Towards Precise Regeneration," Sustainability, MDPI, vol. 17(19), pages 1-24, September.
    4. Li Tu & Xiao Jiang & Yixing Guo & Qi Qin, 2025. "Effect of Visual Quality of Street Space on Tourists’ Stay Willingness in Traditional Villages—Empirical Evidence from Huangcun Village Based on Street View Images and Machine Learning," Land, MDPI, vol. 14(8), pages 1-19, August.

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