From Physical Risk to Psychological Perception: A Street-View Semantic Segmentation and GIS-Based Study of Micro-Scale Built Environment and Emotional Responses to Urban Pluvial Flooding

With increasingly frequent extreme rainfall and high-density urban development, urban pluvial flooding has become a major challenge to public safety in coastal built-up areas. Existing studies have mainly focused on hydrological and engineering factors such as rainfall, drainage, and topography, while paying limited attention to the heterogeneity of the micro-scale built environment around flood-risk sites and its statistical associations with residents’ average psychological responses. Taking 78 flood-risk buffers in the inland built-up area of Zhuhai as the study area, this study develops an integrated framework combining street-view semantic segmentation, topographic indicator extraction, entropy weighting, cluster analysis, questionnaire surveys, and multiple linear regression. Based on 2351 street-view sampling points, 9404 street-view images, and 9508 valid questionnaires, eight environmental indicators were extracted and aggregated to the buffer level to examine their statistical associations with average perceived emotional stress and negative anxiety. The results identify five typical micro-risk scenarios and show that water exposure, barrier proxy, and building enclosure are the most discriminative variables. Regression analysis further indicates that buffers with higher water exposure, barrier proxy, and building enclosure tend to report higher average perceived emotional stress and negative anxiety, whereas buffers with higher green view index tend to report lower average psychological burden. These findings suggest that urban pluvial flooding is not only a hydrological-engineering issue, but also a compound urban risk that is visualized, spatialized, and contextualized at the street-view scale. This study contributes by shifting flood research from flood-generating factors to buffer-level risk scenarios and physical–psychological association patterns, offering a replicable framework for integrating street-view, GIS, and social perception data.