The spatial equity of outdoor evacuation sites designated for emergency evacuation must be secured. In particular, public administrators must ensure spatial equity in preparing for unpredictable evacuation demands, such as earthquakes. This study analyzed the spatial equity of earthquake evacuation shelters in Daegu, South Korea, by using population data at the local level by time- and date-based mobile phone location data (i.e., floating population data). We compared our analysis of the spatial equity and error rate in this case with census data. Ultimately, our results demonstrate that the use of census population data can cause significant error in evaluations of the equity of evacuation shelter accessibility when the floating population data acquired through mobile phone location data are assumed exact.

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.