## 6 Discussion  
Through the comparison with other methods of damage roof detection, the F value of this classification method was found to be good. The comparison studies considered the entire south of Chiba Prefecture after Typhoon Faxai in 2019 as the test area object and aerial photos. As a method of image analysis, it mainly refers to the methods of [PERSON] et al. [30] and [PERSON] et al. [10]. The method of CNN refers to the method of [PERSON] et al. [13]. Table 7 shows the different classification methods and evaluation indexes compared. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Compared** & **Method of** & **Number of** & **Parts of Classification** & **Accuracy** & **Average** & **F Value** & **Average** \\
**Article** & **Classification** & **Parts** & & **Accuracy** & **F Value** & **F Value** \\ \hline [PERSON] et al. & & & & & & & \\
[7] & visual inspection & 2 parts & roof without blue tarps & 1.000 & 1.000 & 1.000 & 1.000 \\  & & & roof covered with blue tarps & 1.000 & 1.000 & 1.000 & 1.000 \\ \hline [PERSON] et al. [10] & image analysis & 2 parts & roof without blue tarps & 0.746 & 0.746 & 0.738 & 0.738 & \\  & & & roof covered with blue tarps & 0.929 & 0.800 & 0.844 & 0.791 \\ \hline [PERSON] et al. & & & roof without blue tarps & 0.920 & & 0.935 & \\
[30] & image analysis & 3 parts & roof partially covered with blue tarps & 0.810 & 0.880 & 0.791 & 0.801 \\  & & & roof mostly covered with blue tarps & 0.720 & & 0.677 & \\ \hline [PERSON] et al. [13] & Deep learning (CNN) & 3 parts & roof without damage & 0.926 & & 0.955 & \\  & & & roof with blue tarps & 0.964 & 0.937 & 0.944 & 0.730 \\  & & & roof completely destroyed & 0.833 & & 0.291 & \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c} \hline \hline  & & & roof without damage & 0.916 & & 0.951 & \\  & & roof covered with 0-10\% blue tarps & 0.986 & & 0.829 & \\  & & roof covered with 10-50\% blue tarps & 0.987 & 0.976 & 0.819 & 0.818 \\  & & & roof covered with 50-100\% blue tarps & 0.996 & & 0.684 & \\  & & roof completely destroyed & 0.996 & & 0.808 & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison with other classification methods. Figure 17: Distribution of roof damage in southern Chiba Prefecture. (**a**) the total area the detected roof damage in each mesh. (**b**) the total area of the detected roof outline. (**c**) the distribution of the ratio of roof damage. In addition to the accuracy (accuracy and F value) determined, the time required for analysis and universality was also investigated. First, although the method of visual inspection offers high accuracy, it is time consuming. The author initially extracted damaged roofs from aerial photos in the southern area of Chiba prefecture through visual inspection, which required more than 2 days. Next, employing the method of image analysis, the spectral characteristics of blue tarps were studied, and different parameters were used to extract blue tarps. This method also requires time and lacks universality. Training in the CNN-based method is time consuming, while the time taken for automatic detection is relatively negligible. For example, using a workstation with 64 GB RAM, Windows 10 Pro for workstations, and a 64-bit Quadro RTX 5000 graphics card as the training environment, approximately 3 h are required to train a 10,000 \(\times\) 10,000 area, while it only takes 245 s to detect roof damage at the same place using the trained model; however, in the comparison of various methods about time consumption, owing to the differences between the algorithms and computer hardware used, particularly, the time to prepare the data set and the time to determine which method is the bestcan not be calculated clearly, the specific comparison in time consumption become difficult. Using the same workstation as mentioned above, it takes 245 s to detect a 10,000 \(\times\) 10,000 area and only 10 s to classify damage level using the detected results at the same area.