Street view imagery-based built environment auditing tools: a systematic review

Abstract

The use of street view imagery (SVI) and advanced urban visual intelligence technologies has revolutionized built environment auditing (BEA) practice, by enabling high-resolution BEA at large scales. This study reviewed 96 studies of BEA published before October 2023. The Google SVI was employed in 92.7% of the included studies. Manual processing of SVI was used in BEA in most studies (81.3%), while deep learning methods were mostly used in the remaining studies. Validated auditing tools were used in 71% of the studies. Streets were the most frequently audited objects (54.2%), followed by sidewalk (51%), traffic (49%), and land use (34.4%). Objective attributes exhibited higher reliability in BEA, compared to subjective attributes (e.g. neighborhood environmental perception). The Active Neighborhood Checklist and Microscale Audit of Pedestrian Streetscapes were the two most widely used SVI-based BEA tools. Several key areas for improving the accuracy and reliability of SVI-based BEA were identified: building standardized datasets of built environment features for more accurate auditing, combining multi-source SVI for more comprehensive assessments, and adapting auditing tools to the contexts in developing countries. This study would contribute to a deeper understanding of built environmental influences on health, and facilitate informed decision-making in urban planning and public health efforts.