Deep Learning-Based Systems for Evaluating and Enhancing Child-Friendliness of Urban Streets—A Case of Shanghai Urban Street - Huijun Tu; Xudong Miao; Shitao Jin; Jiayi Yang; Xinyue Miao; Jiale Qi (Buildings, 2025)
In the context of rapid urbanization, urban streets have become critical spatial environments for children’s daily activities, directly influencing their mobility safety, behavioral development, and the spatial equity of cities. However, conventional assessment methods largely rely on subjective surveys and qualitative analyses, lacking objectivity and scalability. To address these limitations, this study takes urban streets in Shanghai as a case study and integrates deep learning technologies to propose a generalizable methodology for developing a child-friendliness evaluation and enhancement system that incorporates multi-source data and perceptual indicators for urban streets. The system extracts spatial features of streets based on urban street environmental information, and incorporates evaluation inputs from intergenerational user groups, including children and their caregivers. A neural network model is trained to enable automated, multidimensional assessment of child-friendliness and to generate context-sensitive and adaptable strategies. The findings reveal significant perceptual differences between user groups: children place greater emphasis on playfulness and interactivity, while caregivers prioritize safety and comfort. This validates the necessity and effectiveness of adopting an intergenerational collaborative perspective for comprehensive child-friendliness evaluation. By overcoming the limitations of traditional approaches in terms of accuracy and efficiency, this research expands the methodological repertoire of child-friendly urban studies and provides data-driven support for the intelligent design and inclusive governance of urban streets.