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

2025-08-13

AI Summary

This study addresses the challenge of ensuring street-space inclusivity amid shifting urban sociodemographic structures. We propose a hybrid evaluation framework integrating participatory methods with artificial intelligence. Leveraging in-depth resident interviews and Mapillary street-view imagery, we construct a labeled dataset capturing diverse group perceptions; then apply computer vision, machine learning, and spatial heat-map analysis to generate physical-feature–public-perception association maps. Our key contributions are: (1) a co-production mechanism and fine-grained annotation strategy for population-specific spatial needs, significantly improving AI model accuracy in detecting spatial experiences of marginalized groups; and (2) empirical identification of intergroup perceptual disparities across safety, accessibility, and sense of belonging. Results validate the framework’s efficacy in enhancing the systematicity, equity, and operationalizability of public-space assessment—providing empirical evidence and a scalable technical pathway for inclusive urban design and governance.

Technology Category

• Humans and AI: AI for Accessibility
• Computer Vision: Bias, Fairness & Privacy
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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 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.

Problem

• Assessing streetscape inclusivity through participatory AI methods
• Correlating subjective user ratings with physical street attributes
• Enhancing machine learning models with diverse demographic feedback

Innovation

• Participatory research combined with AI analysis
• Visual analytics correlating subjective ratings with attributes
• Co-production strategies enhancing machine learning models