Street Review: A Participatory AI-Based Framework for Assessing Streetscape Inclusivity

TL;DR

Street Review combines participatory research and AI analysis of street images to evaluate inclusivity and accessibility in urban streets, revealing demographic differences and aiding urban planning.

Contribution

It introduces a novel mixed-methods framework integrating community feedback with AI-driven visual analytics for streetscape assessment.

Findings

Perceptions of inclusivity vary across demographic groups.

AI models can be improved through diverse user feedback and careful data-labeling.

The framework supports urban planning and policy development.

Abstract

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 Montr\'eal, 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.