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Research on Urban Street Spatial Quality Based on Street View Image Segmentation

Author

Listed:
  • Liying Gao

    (College of Civil Engineering, Hunan University, Changsha 410000, China)

  • Xingchao Xiang

    (College of Civil Engineering, Hunan University, Changsha 410000, China)

  • Wenjian Chen

    (College of Civil Engineering, Hunan University, Changsha 410000, China)

  • Riqin Nong

    (College of Civil Engineering, Hunan University, Changsha 410000, China)

  • Qilin Zhang

    (College of Civil Engineering, Hunan University, Changsha 410000, China)

  • Xuan Chen

    (School of Architecture and Planning, Hunan University, Changsha 410000, China)

  • Yixing Chen

    (College of Civil Engineering, Hunan University, Changsha 410000, China
    Key Laboratory of Building Safety and Energy Efficiency of the Ministry of Education, Hunan University, Changsha 410000, China)

Abstract

Assessing the quality of urban street space can provide suggestions for urban planning and construction management. Big data collection and machine learning provide more efficient evaluation methods than traditional survey methods. This study intended to quantify the urban street spatial quality based on street view image segmentation. A case study was conducted in the Second Ring Road of Changsha City, China. Firstly, the road network information was obtained through OpenStreetMap, and the longitude and latitude of the observation points were obtained using ArcGIS 10.2 software. Then, corresponding street view images of the observation points were obtained from Baidu Maps, and a semantic segmentation software was used to obtain the pixel occupancy ratio of 150 land cover categories in each image. This study selected six evaluation indicators to assess the street space quality, including the sky visibility index, green visual index, interface enclosure index, public–facility convenience index, traffic recognition, and motorization degree. Through statistical analysis of objects related to each evaluation indicator, scores of each evaluation indicator for observation points were obtained. The scores of each indicator are mapped onto the map in ArcGIS for data visualization and analysis. The final value of street space quality was obtained by weighing each indicator score according to the selected weight, achieving qualitative research on street space quality. The results showed that the street space quality in the downtown area of Changsha is relatively high. Still, the level of green visual index, interface enclosure, public–facility convenience index, and motorization degree is relatively low. In the commercial area east of the river, improvements are needed in pedestrian perception. In other areas, enhancements are required in community public facilities and traffic signage.

Suggested Citation

  • Liying Gao & Xingchao Xiang & Wenjian Chen & Riqin Nong & Qilin Zhang & Xuan Chen & Yixing Chen, 2024. "Research on Urban Street Spatial Quality Based on Street View Image Segmentation," Sustainability, MDPI, vol. 16(16), pages 1-25, August.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:7184-:d:1460912
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    References listed on IDEAS

    as
    1. Jingpeng Duan & Jianjun Liao & Jing Liu & Xiaoxuan Gao & Ailin Shang & Zhihuan Huang, 2023. "Evaluating the Spatial Quality of Urban Living Streets: A Case Study of Hengyang City in Central South China," Sustainability, MDPI, vol. 15(13), pages 1-16, July.
    2. Yang, Jingjing & Deng, Zhang & Guo, Siyue & Chen, Yixing, 2023. "Development of bottom-up model to estimate dynamic carbon emission for city-scale buildings," Applied Energy, Elsevier, vol. 331(C).
    3. Lingzhu Zhang & Yu Ye & Wenxin Zeng & Alain Chiaradia, 2019. "A Systematic Measurement of Street Quality through Multi-Sourced Urban Data: A Human-Oriented Analysis," IJERPH, MDPI, vol. 16(10), pages 1-24, May.
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