## 5 Conclusions  
Although ensuring pedestrian safety near construction sites is a critical responsibility in construction safety management, the illegal encroachment of construction materials and equipment onto sidewalks continues to place pedestrians in hazardous situations. This study quantitatively investigated how changes in the pedestrian environment caused by construction-related objects influence perceived risk levels, using segmentation masks generated by the SAM. To collect pedestrian safety evaluation data, an online survey was conducted in which 42 participants rated their perceived risk for each of the 61 street images. The statistical analysis bridging subjective risk ratings and objective measures can be summarized into three key findings. First, the presence or absence of obstructions and barriers significantly influenced the perceived risk. Second, while the degree of sidewalk obstruction showed a strong positive correlation with the perceived risk, the relative size of barriers did not significantly affect the perceived risk. Third, certain visible characteristics in street images, such as the obstruction type and the presence of construction workers, indirectly influenced the perceived risk.  
This study provides a better understanding of how temporary obstructions and barriers affect pedestrians' perceived risk near construction sites. This study also offers an objective method to assess visible hazards by utilizing advanced image segmentation techniques. Despite these contributions, several limitations remain. First, this study relied solely on image-based evaluations and was unable to capture pedestrian behavior in real-world settings. Second, although the sample street images were carefully selected, the range of visual factors considered was relatively limited. Third, the segmentation accuracy of the fully automated SAM model under complex real-world conditions has not yet been validated. Fourth, the limited sample size and lack of demographic diversity may restrict the generalizability of the findings. Therefore, future studies should focus on developing real-time surveillance systems to monitor unsafe pedestrian behaviors induced by construction activities through CCTV data. In particular, such systems should incorporate various visual features, including barrier height, color, material type, and other urban elements, to deepen our understanding of risk perception. Methodological advancements, such as the application of few-shot learning algorithms, are also necessary to enhance segmentation accuracy under diverse real-world conditions. Additionally, future studies should recruit larger and more diverse samples to better examine how demographic characteristics influence pedestrians' perception of risk.  
Conceptualization, T.Y. and S.L.; methodology, S.L.; data curation, T.Y.; formal analysis, T.Y.; validation, M.C. and S.L.; writing--original draft preparation, T.Y.; writing--review and editing, M.C. and S.L.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Incheon National University (International Cooperative) Research Grant in 2022 (No. 2022-0262).  
**Institutional Review Board Statement:** The study was approved by the Institutional Review Board of Incheon National University (Approval No.: 7007971-202305-010A and Date of Approval: 28 June 2023).  
**Informed Consent Statement:** Informed consent for participation was obtained from all subjects involved in the study.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors upon request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.