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

arXiv: https://arxiv.org/abs/2508.11708 · HTML: https://mlsanity.com/paper/2508.11708/

## 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 (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Power to the People? Opportunities and Challenges for Participatory AI](https://mlsanity.com/paper/2209.07572/) (2022)
- [Deep Learning the City : Quantifying Urban Perception At A Global Scale](https://mlsanity.com/paper/1608.01769/) (2016)
- [Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation](https://mlsanity.com/paper/2205.04610/) (2022)
- [A Close Look at Deep Learning with Small Data](https://mlsanity.com/paper/2003.12843/) (2020)
- [`It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Values](https://mlsanity.com/paper/2307.10221/) (2023)
- [A citizen science toolkit to collect human perceptions of urban environments using open street view images](https://mlsanity.com/paper/2403.00174/) (2024)
- [LIVS: A Pluralistic Alignment Dataset for Inclusive Public Spaces](https://mlsanity.com/paper/2503.01894/) (2025)
- [Negotiative Alignment: Embracing Disagreement to Achieve Fairer Outcomes -- Insights from Urban Studies](https://mlsanity.com/paper/2503.12613/) (2025)
- [The Right to AI](https://mlsanity.com/paper/2501.17899/) (2025)
- [Co-Producing AI: Toward an Augmented, Participatory Lifecycle](https://mlsanity.com/paper/2508.00138/) (2025)

## Built on by (most cited first, 3 of 3)

- [Do Vision-Language Models See Urban Scenes as People Do? An Urban Perception Benchmark](https://mlsanity.com/paper/2509.14574/) (2025)
- [Traceable, Enforceable, and Compensable Participation: A Participation Ledger for People-Centered AI Governance](https://mlsanity.com/paper/2602.10916/) (2026)
- [AI Pluralism and the Worlds It Misses](https://mlsanity.com/paper/2606.16167/) (2026)

## 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.