Eyes on the Street: Assessing Window-to-Wall Ratios in Google Street Views using Machine Learning

Eyes on the Street: Assessing Window-to-Wall Ratios in Google Street Views using Machine Learning

Tu, Han

Full Article:

Windows play an important role in ‘Eyes on the Street’ in Jane Jacobs’ theory. However, vital street-level parameters in her theory, most notably windows, are rarely assessed at the urban scale due to imprecise existing datasets. To resolve this challenge, this study proposes an automated computer vision-based methodology to extract the window-to-wall ratios (WWRs) of buildings in the Bronx, New York, using semantic segmentation machine learning. This study brings together machine learning and Google Street View (GSV) to accurately assess WWRs at the urban scale. The WWR distribution results show that street-level WWRs help to analyze with other urban data, with controlled parameters, such as land use and building age. Our WWR assessment can be universally applied to other cities using geotagged street view imagery of GSV. This study can help provide a reference for precise future urban design and management assessments.

Windows play an important role in ‘Eyes on the Street’ in Jane Jacobs’ theory. However, vital street-level parameters in her theory, most notably windows, are rarely assessed at the urban scale due to imprecise existing datasets. To resolve this challenge, this study proposes an automated computer vision-based methodology to extract the window-to-wall ratios (WWRs) of buildings in the Bronx, New York, using semantic segmentation machine learning. This study brings together machine learning and Google Street View (GSV) to accurately assess WWRs at the urban scale. The WWR distribution results show that street-level WWRs help to analyze with other urban data, with controlled parameters, such as land use and building age. Our WWR assessment can be universally applied to other cities using geotagged street view imagery of GSV. This study can help provide a reference for precise future urban design and management assessments.

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DOI: 10.5151/sigradi2022-sigradi2022_35

Referências bibliográficas

• [1]

Como citar:

Tu, Han; "Eyes on the Street: Assessing Window-to-Wall Ratios in Google Street Views using Machine Learning", p-175-186. In: XXVI International Conference of the Iberoamerican Society of Digital Graphics. São Paulo: Blucher, 2023.
ISSN 23186968, DOI 10.5151/sigradi2022-sigradi2022_35