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

Research in ENGLISH

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.
Machine Learning, Data Analytics, Google Street View (GSV), Visual quality, Window-to-wall ratio