Street Review: A Participatory AI-Based Framework for Assessing Streetscape Inclusivity
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 15 of its 111 references that are in the map. First posted on arXiv 14 Aug 2025.
Builds on
15 of 111 references.
References · 15
- 2017Attention Is All You Needcited by 60,361
- 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
- 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,940
- 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
- 2018Datasheets for Datasetscited by 1,386
- 2022Power to the People? Opportunities and Challenges for Participatory AIcited by 138
- 2016Deep Learning the City : Quantifying Urban Perception At A Global Scalecited by 65
- 2022Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluationcited by 59
- 2020A Close Look at Deep Learning with Small Datacited by 54
- 2023`It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Valuescited by 18
- 2024A citizen science toolkit to collect human perceptions of urban environments using open street view imagescited by 9
- 2025LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spacescited by 8
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.