Analyzing the effects of Green View Index of neighborhood streets on walking time using Google Street View and deep learning

Donghwan Ki, Sugie Lee

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Previous research has reported that greenery is an important factor in walking activities, with greenery existing in various forms, including trees, gardens, green walls, and other examples. However, traditional methods of measuring urban greenery involve limitations in coverage of various forms of greenery and do not reflect the actual degree of exposure to pedestrians. Accordingly, this study examined the street Green View Index (GVI) and its associations with walking activities by different income groups using survey data on walking behaviors in 2350 residents in Seoul, Korea. This study utilized Google Street View (GSV) and deep learning to calculate the GVI by semantic segmentation, referring to greenness from the visual perspective of pedestrians. Correlation analyses between traditional greenery variables and GVI were conducted to examine differences, and multiple regression models were applied to identify the relationships between walking time and greenery variables. The results of this study show differences between conventional greenery variables and GVI in terms of specific greenery forms and perspectives. As hypothesized, GVI was more closely associated with walking time than the traditional greenery variables. Also, this study found that the low-income residents generally lived in low GVI neighborhood, but walking time is more sensitive to GVI. These results were because GVI represents the actual greenery exposure to pedestrians, and there was a difference between income groups in the degree of vehicle usage in daily life. The results of this study indicate that, when analyzing the relationship between urban greenness and walking behavior, it is necessary to examine the relationship from multiple angles and to investigate the importance of eye-level street greenery. Our findings provide useful insights for public policies to promote pedestrian walking environments.

Original languageEnglish
Article number103920
JournalLandscape and Urban Planning
StatePublished - 2021 Jan


  • Deep learning
  • Google Street View
  • Green View Index
  • Semantic segmentation
  • Walking time

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