Predicting walking-to-work using street-level imagery and deep learning in seven Canadian cities.

ABSTRACT: New 'big data' streams such as street-level imagery are offering unprecedented possibilities for developing health-relevant data on the urban environment. Urban environmental features derived from street-level imagery have been used to assess pedestrian-friendly neighbourhood design and to predict active commuting, but few such studies have been conducted in Canada. Using 1.15 million Google Street View (GSV) images in seven Canadian cities, we applied image segmentation and object detection computer vision methods to extract data on persons, bicycles, buildings, sidewalks, open sky (without trees or buildings), and vegetation at postal codes. The associations between urban features and walk-to-work rates obtained from the Canadian Census were assessed. We also assessed how GSV-derived urb

SUBMITTER: Doiron D 

PROVIDER: S-EPMC9626470 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature