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

City streets, sidewalks, and public areas often serve as primary interaction points among diverse user groups, including residents, commuters, and visitors ( Gehl, 2011). These spaces carry social, economic, and cultural signifi - cance that influences navigation and user experience ( Mitra ˇ sinovi c & Mehta, 2021). Municipal governments and planning agencies recognize the importance of inclusive public spaces but face challenges in operation - alizing inclusivity ( Anttiroiko & De Jong, 2020). Traditional approaches may draw on universal design principles intended to accommodate a broad range of users, but these frameworks often take a one-size-fits-all approach that prioritizes physical accessibility over the social and cul - tural dimensions of public space use ( Low, 2020). In multicultural cities, where multiple languages, cultures, and religious practices converge, these complexities become particularly evident ( Fan et al., 2023; Lit - man, 2025; Salgado et al., 2021; Youngbloom et al., 2023). Research on inclusive design has provided valuable insights, but few methods combine qualitative depth with quantitative scale to under - stand inclusivity in urban contexts ( Anttiroiko & De Jong, 2020; Mehta, 2019; Zamanifard et al., 2019). Ethnographic research and interviews offer detailed perspectives on lived experience, while computer vision and machine learning enable assessments at larger scales ( Ibrahim et al., 2020). However, large-scale computational approaches often overlook intersectional dimensions ( Zhu et al., 2025). This gap calls for integrated models that merge qualitative and quantitative methodologies.