Development of a machine-simulated human scoring model for assessing child-friendly street environments: A case study of Sham Shui Po, Hong Kong SAR, China

With a growing interest in liveable cities, scholars and urban planners are increasingly studying the characteristics of child-friendly cities, including the ability to walk and move freely in public spaces. While machine learning techniques and street view imagery analysis have enabled the systematic analysis of streets, they have not yet been applied to assess street environments from a child’s perspective. This study explores the use of deep learning models to address this gap by developing a machine-simulated human scoring model to assess health and safety indicators in urban streets. Using a high-density, old urban district in Hong Kong SAR, China, as a case, the study used semantic segmentation to analyze street environmental features and extract elements related to safety, such as greenery, vehicles, and fences. Subsequently, the model generated safety ratings, which were compared with scores provided by volunteer caregivers. The results indicate that natural elements and fences enhance safety, whereas an excess of buildings diminishes it. In contrast to European cities, where high visibility and larger sky proportions are considered beneficial for health, these factors were less relevant in the high-density, tropical context of Hong Kong. This analysis highlights the robustness and efficiency of the model, which can assist researchers in other cities in collecting empirical user rating data and informing strategies for more child-friendly urban planning.