## 1 Introduction
### Previous Research  
After the 1960s, the use of aerial photographs in field investigations after typhoons, earthquakes, tsunamis and other disasters gained traction. Aerial photographs can provide local information with rich surface texture and spectral characteristics. Rapid and effective house damage extraction has been one of the most important research contents in the field of aerial photo processing. For example, [PERSON] et al. [6] successfully measured the damaged area of houses in the 1995 Hanshin earthquake with aerial photos. There are three extraction methods of roof damage detection following a disaster based on aerial photos. The first method is visual inspection (1970-2000). The damage extraction is primarily performed manually by architects, emergency risk judges, and other civil field experts. For example, [PERSON] et al. [7] conducted a field investigation using a small aircraft after typhoon JEBI in 2018. Considering the south of Osaka Prefecture and the north of Wakayama Prefecture as the survey areas, they investigated the distribution of blue tarps covered on residents' roofs. The results indicated a linear relationship between the number of blue tarps, such that, the number of damaged houses can be estimated using the number of blue tarps. [PERSON] et al. [8] successfully detected houses with blue tarps covering the roof, using the aerial photos taken after tornadoes in Tsukuba, adhering to the criteria for damage identification issued by the Japan cabinet office. However, although this method offered high accuracy, it often requires several days. The second method is image analysis (2000-2015), which is based on the pixel, threshold of the spectrum, brightness, and texture of aerial photos to judge house damage. When the threshold is exceeded, roof damage is concluded to have occurred. For example, [PERSON] et al. [9] used the aerial photos taken after typhoon Jebi in 2018 to assess the roof damage of residential houses depending on whether blue tarps were covered, and used the results to infer the damage rate of the roof. In addition, [PERSON] et al. [10] established a method of identifying the damage to residential roofs in a wide area using aerial photos acquired after Typhoon Faxai in 2019. In addition, it was compared to visual inspection results and its accuracy was confirmed as efficient. [PERSON] et al. [11] used the aerial photos taken immediately after the Kumamoto earthquake, and used DSM analysis and texture analysis methods to extract the roofs covered with blue tarps. However, this method does not provide clear technical points regarding threshold setting. The third method is deep learning (2015 and beyond) [12], wherein a single house is considered as the extraction object. For example, [PERSON] et al. [13] used the aerial photo of the 1995 southern Hyogo and 2016 Kumamoto earthquakes to train a deep learning model. The model can be used to automatically classify the damage grade of houses into three levels: collapsed houses, non-collapsed houses, and houses covered with blue tarps. Further, [PERSON] verified the accuracy of the model at Kyonan town, Chiba Prefecture \begin{table}
\begin{tabular}{c c c} \hline \hline
**Investigation** & **Total** & **Percentage** \\ \hline Surveyed houses & 1976 & - \\ Damaged house & 1133 & 57\% \\ Damaged Roof & 902 & 46\% \\ Damaged Wall & 228 & 12\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistics of field investigation after typhoon Faxai in 2019.  
after Typhoon Faxai in 2019. [PERSON] et al. [14] presented a deep learning method that incorporates the autoregressive (AR) time series model with two-step artificial neural networks (ANNs) to identify damage under temperature variations. Numerical results indicate that the proposed approach could successfully recognize, locate, and quantify structure damage by using output-only vibration and temperature data regardless of other influencing factors.