## 5 Classification of Roof Damage Level  
The ultimate goal was to use the test model to classify the level of roof damage. Figure 14 shows a flow chart for classification that has been roughly divided into three parts. First is the detection of roof outline, which if not detected is determined to be Non-roof. Thereafter, if the roofs that are completely destroyed are detected. Whether blue tarps are detected later or not, it is directly classified as Level 4. Finally, if blue tarps and the roof outline are detected simultaneously in an area, the proportion of blue tarps in this area can be calculated using the formula provided in Figure 14. Levels 1, 2, and 3 were determined according to the proportion. If only the roof outline was detected out and no blue tarps were detected out, it was classified as level 0. Figure 15 shows the statistical data of each classification stage. The first row is the data counted by the visual inspection of the area surrounded by the green box in Figure 3. There are 7649, 375, 318, 62, and 104 roofs of levels 0, 1, 2, 3, and 4, respectively, as well as several non-roofs. In the detection of the contours of roots in the second row, 7117 houses were correctly detected, 590 houses were missed (most were undamaged roofs, a total of 525 roofs), and 161 houses were wrongly detected (mainly automobiles, agricultural greenhouses, pools, etc.). Further, in the detection of roofs completely destroyed in the third row, 82 roofs completely destroyed were correctly detected, 21 were missed, and 18 were wrongly detected (including outdoor warehouse). In the detection of blue tarps in the fourth row, the detection omission and error detection are displayed below the block. In particular, in row 5, owing to the detection accuracy of blue tarps and roof outlines, although certain objects were detected out successfully, the position deviation was considerably large, resulting in the wrong classification. For example, in column B of row 5, because the area of the detected blue tarps is larger than the actual area, 33 roofs of level 1 are wrongly classified (5 and 29 roofs are wrongly classified as level 3 and 2,  
Figure 14: Flowchart of classification of the level of roof damage. respectively). In row 6, the sum of the results of the final tests is presented. For example, in column C, there are 318 roofs of level 2 of which 247 roofs were correctly classified as level 2, 5 roofs are wrongly classified as level 0, 32 roofs are wrongly classified as level 1, 8 roofs are wrongly classified as level 3, 1 roof is wrongly classified as level 4, and 25 roofs are non-roof. Other detailed statistical results are shown in Table 6. Figure 16 shows the map showing the result of each detected object and the level of roof damage. In Table 6, the accuracy and F value of the classification of each damage level is calculated separately. Then, the accuracy of each level and the average value of the F value were considered as the mean accuracy and F value of classification, respectively. The results show that the overall accuracy was 0.97, and the overall F value was approximately 0.81. This is because the number of levels 0 was considerable at each level. Therefore, the accuracy can be evaluated based on the accuracy rate. However, when the number of different levels varies greatly, the accuracy rate could not be used as a good index. Therefore, the comparison in Chapter 6 mainly compares with other methods using the F value. To inspect the spatial distribution of blue tarps after typhoon Faxai a 250 m mesh was used to render the distribution map according to the benchmark of the Bureau of statistics of Japan [29], and the blue tarps, roof outline, and the ratio of blue tarps were calculated for each mesh. Figure 17 shows the distribution map, where (a) is the total area of the detected roof damage in each grid (sq m). Most of the blue tarps were distributed in Minamiboso City, the central part, near the Mera area and Tateyama city. Further, (b) is the total area of the detected roof outline (sq m).