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This study aimed to investigate the relationship between flood safety awareness and the vulnerability/resilience associated with flood damage, focusing on the characteristics of regions that have experienced floods. Specifically, this study addresses the following research questions: (1) How does flood safety awareness vary among regions with different levels of vulnerability and resilience? (2) What patterns emerge in the relationship between regional characteristics and residents' flood safety awareness? A fuzzy analysis was used by selecting structural and social indicators to analyze vulnerability and resilience. A survey was conducted for each region to assess flood safety awareness among residents. The analysis results showed that vulnerability and resilience exhibit three distinct patterns according to regional characteristics, which correspond to the flood safety awareness of residents. Regions with low vulnerability and high resilience demonstrated high safety awareness, whereas regions with high vulnerability and low resilience demonstrated low safety awareness. This study lies in its comparative analysis of flood safety awareness across different regional contexts and its use of fuzzy analysis to provide a nuanced understanding of vulnerability and resilience. This study accurately identifies problems and regional differences by analyzing vulnerability and resilience and establishing preventive measures that fit regional characteristics. Furthermore, this study provides a guideline for suggesting effective disaster response strategies, contributing new insights into disaster preparedness and community resilience.

The detection of asbestos roof slate by drone is necessary to avoid the safety risks and costs associated with visual inspection. Moreover, the use of deep-learning models increases the speed as well as reduces the cost of analyzing the images provided by the drone. In this study, we developed a comprehensive learning model using supervised and unsupervised classification techniques for the accurate classification of roof slate. We ensured the accuracy of our model using a low altitude of 100 m, which led to a ground sampling distance of 3 cm/pixel. Furthermore, we ensured that the model was comprehensive by including images captured under a variety of light and meteorological conditions and from a variety of angles. After applying the two classification methods to develop the learning dataset and employing the as-developed model for classification, 12 images were misclassified out of 475. Visual inspection and an adjustment of the classification system were performed, and the model was updated to precisely classify all 475 images. These results show that supervised and unsupervised classification can be used together to improve the accuracy of a deep-learning model for the detection of asbestos roof slate.