用用户参与+AI分析评估街道包容性，助力城市规划。
Street Review: A Participatory AI-Based Framework for Assessing Streetscape Inclusivity
- 结合居民访谈与4.5万张街景图，用AI分析街道特征。
- 发现不同人群对街道包容性的感知差异显著。
- 适合城市规划者、政策制定者参考使用。
城市中心经历社会、人口和文化变迁，影响公共空间使用，亟需系统评估公共空间。本研究提出Street Review框架，融合参与式研究与基于AI的分析方法，评估街道景观包容性。在加拿大蒙特利尔，28名居民参与半结构化访谈和图像评估，并结合约4.5万张Mapillary街景图像进行分析。该方法生成热力图等可视化结果，将主观评价与人行道、维护状况、绿化和座椅等物理属性关联。研究发现不同人口群体对包容性和可达性的感知存在差异，表明通过精心标注和共同生产策略整合多元用户反馈，可提升机器学习模型性能。Street Review为城市规划者和政策分析师提供系统方法，支持街道规划、政策制定与管理。
原文摘要 · Abstract (English)
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.
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