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

Duy Thong Ta and Katsunori Furuya ()
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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

Land, 2022, vol. 11, issue 12, 1-18

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.

Keywords: technological innovations; informal green space; Google Street View; machine learning; urban planning and development; ArcGIS; Ichikawa; Ho Chi Minh; density heatmap (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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