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Google Street View and Machine Learning—Useful Tools for a Street-Level Remote Survey: A Case Study in Ho Chi Minh, Vietnam and Ichikawa, Japan

Author

Listed:
  • Duy Thong Ta

    (Department of Environment Science and Landscape Architecture, Graduate School of Horticulture, Chiba University, Matsudo 271-8510, Chiba, Japan)

  • Katsunori Furuya

    (Department of Environment Science and Landscape Architecture, Graduate School of Horticulture, Chiba University, Matsudo 271-8510, Chiba, Japan)

Abstract

This study takes one step further to complement the application of a method for mapping informal green spaces (IGSs) using an efficient combination of open-source data with simple tools and algorithms. IGSs are unofficially recognized by the government as vegetation spaces designed for recreation, gardening, and forestry in urban areas. Due to the economic crisis, many formal green spaces such as urban parks and garden projects have been postponed, while IGSs have significant potential as green space retrofits. However, because they are small and spatially continuous and cannot be fully detected via airborne surveys, they are surveyed in small areas and neglected by government and city planners. Therefore, in this research, we combined the use of Google Street View (GSV) data with machine learning to develop a survey method that can be used to survey a wide area at once. Deeplab V3+ was used to segment the semantics based on the model created using 1000 labelled photos, with an accuracy rate of nearly 65%. Applying this method gave high accuracy in Ichikawa, Japan, with 3029 photos, and matched the results of a field survey in a previous study. In contrast, low accuracy was seen in Ho Chi Minh City, with 204 photos, where the quality of the GSV data was considerably lower.

Suggested Citation

  • Duy Thong Ta & Katsunori Furuya, 2022. "Google Street View and Machine Learning—Useful Tools for a Street-Level Remote Survey: A Case Study in Ho Chi Minh, Vietnam and Ichikawa, Japan," Land, MDPI, vol. 11(12), pages 1-18, December.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:12:p:2254-:d:999184
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    References listed on IDEAS

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    1. Minseo Kim & Christoph D. D. Rupprecht & Katsunori Furuya, 2018. "Residents’ Perception of Informal Green Space—A Case Study of Ichikawa City, Japan," Land, MDPI, vol. 7(3), pages 1-20, September.
    2. Lucy E. Keniger & Kevin J. Gaston & Katherine N. Irvine & Richard A. Fuller, 2013. "What are the Benefits of Interacting with Nature?," IJERPH, MDPI, vol. 10(3), pages 1-23, March.
    3. Rupprecht, Christoph, 2017. "Informal urban green space: Residents’ perception, use, and management preferences across four major Japanese shrinking cities," SocArXiv ug86b, Center for Open Science.
    4. Megan Heckert & Jeremy Mennis, 2012. "The Economic Impact of Greening Urban Vacant Land: A Spatial Difference-In-Differences Analysis," Environment and Planning A, , vol. 44(12), pages 3010-3027, December.
    5. Elmira Nasri Roodsari & Parian Hoseini, 2022. "An assessment of the correlation between urban green space supply and socio-economic disparities of Tehran districts—Iran," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(11), pages 12867-12882, November.
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    Cited by:

    1. Jaloliddin Rustamov & Zahiriddin Rustamov & Nazar Zaki, 2023. "Green Space Quality Analysis Using Machine Learning Approaches," Sustainability, MDPI, vol. 15(10), pages 1-25, May.

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