Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Training Computers to See the Built Environment Related to Physical Activity: Detection of Micro-Scale Walkability Features Using Computer Vision

Version 1 : Received: 1 March 2022 / Approved: 3 March 2022 / Online: 3 March 2022 (13:49:08 CET)

A peer-reviewed article of this Preprint also exists.

Adams, M.A.; Phillips, C.B.; Patel, A.; Middel, A. Training Computers to See the Built Environment Related to Physical Activity: Detection of Microscale Walkability Features Using Computer Vision. Int. J. Environ. Res. Public Health 2022, 19, 4548. Adams, M.A.; Phillips, C.B.; Patel, A.; Middel, A. Training Computers to See the Built Environment Related to Physical Activity: Detection of Microscale Walkability Features Using Computer Vision. Int. J. Environ. Res. Public Health 2022, 19, 4548.

Journal reference: Int. J. Environ. Res. Public Health 2022, 19, 4548
DOI: 10.3390/ijerph19084548

Abstract

The study purpose was to train and validate a deep-learning approach to detect micro-scale streetscape features related to pedestrian physical activity. This work innovates by combining computer vision techniques with Google Street View (GSV) images to overcome impediments to conducting audits (e.g., time, safety, and expert labor cost). The EfficientNETB5 architecture was used to build deep-learning models for eight micro-scale features guided by the Microscale Audit of Pedestrian Streetscapes-Mini tool: sidewalks, sidewalk buffers, curb cuts, zebra and line crosswalks, walk signals, bike symbols, and streetlights. We used a train--correct loop, whereby images were trained on a training dataset, evaluated using a separate validation dataset, and trained further until acceptable performance metrics were achieved. Further, we used trained models to audit participant (N=512) neighborhoods in the WalkIT Arizona trial. Correlations were explored between micro-scale features and GIS-measured- and participant reported-macro-scale walkability. Classifier precision, recall, and overall accuracy were all >84%. Total micro-scale was associated with overall macro-scale walkability (r=0.300,p<.001). Positive associations were found between model-detected and self-reported sidewalks (r=0.41,p<.001) and sidewalk buffers (r=0.26,p<.001). Computer vision model results suggest an alternative to trained human raters, allowing for audits of hundreds or thousands of neighborhoods for population surveillance or hypothesis testing.

Keywords

Computer vision; Google Street View; Built Environment; Walkability; Micro-scale; Deep learning

Subject

BEHAVIORAL SCIENCES, Other

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