Street Review: A Participatory AI-Based Framework for Assessing Streetscape Inclusivity
It provides a systematic method for urban planners and policy analysts to evaluate public streets, though it is incremental as it builds on existing participatory and AI techniques.
This study tackled the problem of assessing streetscape inclusivity by developing Street Review, a framework that combines participatory research with AI-based analysis of street-view images, resulting in visual analytics that correlate user ratings with physical attributes and reveal variations in perceptions across demographic groups.
Urban centers undergo social, demographic, and cultural changes that shape public street use and require systematic evaluation of public spaces. This study presents Street Review, a mixed-methods approach that combines participatory research with AI-based analysis to assess streetscape inclusivity. In Montréal, Canada, 28 residents participated in semi-directed interviews and image evaluations, supported by the analysis of approximately 45,000 street-view images from Mapillary. The approach produced visual analytics, such as heatmaps, to correlate subjective user ratings with physical attributes like sidewalk, maintenance, greenery, and seating. Findings reveal variations in perceptions of inclusivity and accessibility across demographic groups, demonstrating that incorporating diverse user feedback can enhance machine learning models through careful data-labeling and co-production strategies. The Street Review framework offers a systematic method for urban planners and policy analysts to inform planning, policy development, and management of public streets.