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Research on Green View Index of Urban Roads Based on Street View Image Recognition: A Case Study of Changsha Downtown Areas

Author

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  • Yixing Chen

    (College of Civil Engineering, Hunan University, Changsha 410082, China
    Key Laboratory of Building Safety and Energy Efficiency of the Ministry of Education, Hunan University, Changsha 410082, China)

  • Qilin Zhang

    (College of Civil Engineering, Hunan University, Changsha 410082, China)

  • Zhang Deng

    (College of Civil Engineering, Hunan University, Changsha 410082, China)

  • Xinran Fan

    (School of Architecture and Planning, Hunan University, Changsha 410082, China)

  • Zimu Xu

    (School of Architecture and Planning, Hunan University, Changsha 410082, China)

  • Xudong Kang

    (School of Architecture and Planning, Hunan University, Changsha 410082, China)

  • Kailing Pan

    (School of Architecture and Planning, Hunan University, Changsha 410082, China)

  • Zihao Guo

    (School of Architecture and Planning, Hunan University, Changsha 410082, China
    School of Architecture and Planning, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand)

Abstract

In this paper, we took the urban roads in the Changsha downtown areas as an example to identify the green view index (GVI) of urban roads based on street view images (SVIs). First, the road network information was obtained through OpenStreetMap, and the coordinate information of sampling points was processed using ArcGIS. Secondly, the SVIs were downloaded from Baidu Map according to the latitude and longitude coordinates of the sampling points. Moreover, semantic segmentation neural network software was used to semantically segment the SVIs for recognizing the objects in each part of the images. Finally, the objects related to green vegetation were statistically analyzed to obtain the GVI of the sampling points. The GVI was mapped to the map in ArcGIS software for data visualization and analysis. The results showed the average GVI of the study area was 12.56%. An amount of 27% have very poor green perception, 40% have poor green perception, 19% have general green perception, 10% have strong green perception, and 4% have very strong green perception. In the administrative districts, the highest GVI is Yuhua District with 14.15%, while the lowest is Kaifu District with 8.75%. The average GVI of the new urban area is higher than that of the old urban area, as the old urban area has higher building density and a lower greenery level. This paper systematically evaluated the levels of GVI and greening status of urban streets within the Changsha downtown areas through SVIs data analysis, and provided guidance and suggestions for the greening development of Changsha City.

Suggested Citation

  • Yixing Chen & Qilin Zhang & Zhang Deng & Xinran Fan & Zimu Xu & Xudong Kang & Kailing Pan & Zihao Guo, 2022. "Research on Green View Index of Urban Roads Based on Street View Image Recognition: A Case Study of Changsha Downtown Areas," Sustainability, MDPI, vol. 14(23), pages 1-17, December.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:23:p:16063-:d:990496
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    References listed on IDEAS

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    1. 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.
    2. Yusuke Kumakoshi & Sau Yee Chan & Hideki Koizumi & Xiaojiang Li & Yuji Yoshimura, 2020. "Standardized Green View Index and Quantification of Different Metrics of Urban Green Vegetation," Sustainability, MDPI, vol. 12(18), pages 1-16, September.
    3. Hao Zou & Xiaojun Wang, 2021. "Progress and Gaps in Research on Urban Green Space Morphology: A Review," Sustainability, MDPI, vol. 13(3), pages 1-16, January.
    4. Song Zhao & Agnès Patuano, 2021. "International Chinese Students in the UK: Association between Use of Green Spaces and Lower Stress Levels," Sustainability, MDPI, vol. 14(1), pages 1-23, December.
    5. Zhang Deng & Yixing Chen & Xiao Pan & Zhiwen Peng & Jingjing Yang, 2021. "Integrating GIS-Based Point of Interest and Community Boundary Datasets for Urban Building Energy Modeling," Energies, MDPI, vol. 14(4), pages 1-17, February.
    6. Rencai Dong & Yonglin Zhang & Jingzhu Zhao, 2018. "How Green Are the Streets Within the Sixth Ring Road of Beijing? An Analysis Based on Tencent Street View Pictures and the Green View Index," IJERPH, MDPI, vol. 15(7), pages 1-22, June.
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    Cited by:

    1. Zhihao Kang & Chenfeng Xu & Yang Gu & Lunsai Wu & Zhiqiu He & Xiaoxu Heng & Xiaofei Wang & Yike Hu, 2025. "Exploring the Spatial Coupling Between Visual and Ecological Sensitivity: A Cross-Modal Approach Using Deep Learning in Tianjin’s Central Urban Area," Land, MDPI, vol. 14(11), pages 1-25, October.
    2. Jinting Zhang & Cheng Liu & Min Xu & Sheng Zheng, 2025. "Equity Evaluation of Street-Level Greenery Based on Green View Index from Street View Images: A Case Study of Hangzhou, China," Land, MDPI, vol. 14(8), pages 1-18, August.

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