## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Story Number**} & \multicolumn{6}{c}{**Number of Real Data**} & \multirow{2}{*}{**Total**} & \multirow{2}{*}{**Accuracy Rate (\%)**} \\ \cline{3-3} \cline{5-8}  & & **Industry** & & & **Industry** & & **Low-** & **Middle-** & **High-** & **High-** & **(\%)** \\ \multirow{3}{*}{\begin{tabular}{c} Number of \\ validation \\ data \\ \end{tabular} } & **Story** & **Story** & **Story** & **Story** & **Story** & **Story** & **Story** & **Story** & **Story** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Number of \\ of valid \\ data \\ \end{tabular} } & Industry & 42 & 2 & 0 & 0 & 2 & 0 & 0 & 44 & 93.75 \\  & \begin{tabular}{c} Industry \\ multi-story \\ low-story \\ data \\ \end{tabular} & 1 & 15 & 0 & 0 & 0 & 0 & 0 & 16 & 84.96 \\  & \begin{tabular}{c} Low-story \\ middle-story \\ \end{tabular} & 7 & 1 & 96 & 7 & 0 & 0 & 0 & 113 & 95.45 \\  & \begin{tabular}{c} Middle-story \\ data \\ \end{tabular} & 0 & 0 & 8 & 279 & 12 & 2 & 0 & 301 & 92.69 \\  & \begin{tabular}{c} Mid-high-story \\ high-story \\ \end{tabular} & 0 & 0 & 0 & 9 & 74 & 14 & 0 & 97 & 76.29 \\  & \begin{tabular}{c} High-story \\ Super-high \\ story \\ \end{tabular} & 0 & 0 & 0 & 12 & 11 & 63 & 0 & 86 & 73.26 \\  & \begin{tabular}{c} Story \\ \end{tabular} & 0 & 0 & 0 & 0 & 0 & 2 & 2 & 100.00 \\  & \begin{tabular}{c} Total \\ Accuracy rate (\%) \\ Overall accuracy (\%) \\ Kappa coefficient \\ \end{tabular} & 0 & 0 & 0 & 0 & 0 & 0 & 2 & 2 & 100.00 \\  & \begin{tabular}{c} Total \\ Accuracy rate (\%) \\ Overall accuracy (\%) \\ Kappa coefficient \\ \end{tabular} & 50 & 18 & 104 & 307 & 99 & 79 & 2 & 659 & \\  & \
\begin{tabular}{c} Accuracy rate (\%) \\ Overall accuracy (\%) \\ Kappa coefficient \\ \end{tabular} & 84.00 & 83.33 & 92.31 & 90.88 & 74.75 & 79.75 & 100.00 & & \\  &
\begin{tabular}{c} 86.65 \\ 0.813 \\ \end{tabular} & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Confusion matrix of the story number of the buildings. and public management data; and combines various methods to realize the extraction of information of buildings in urban areas. To a certain extent, it makes up for the lack of research on comprehensive building exposure data, and also can provide the necessary reference base for improving the earthquake insurance risk exposure database, as well as for the structural vulnerability module and loss module in earthquake insurance. In this paper, we take the urban area of Tangshan City as an example; obtain the information of building footprint, function, story number, structure type, and construction age by different methods; and verify the extraction results. With the training and validation of the U-net model, the global accuracy of the building footprint extraction model is 71%. By comparing the results of manually determined real data with the extraction results of this study for a sample of 660 buildings, it was found that the overall accuracy of building function, story number, structure type, and age extraction are 88.62%, 86.65%, 86.49%, and 85.58%, respectively, and the accuracy of the classification was measured by using kappa coefficient, whose values all exceed 0.8. These indicate that the results of extracting information about buildings using the method of this study can be trusted. The results of the study mainly reveal that the extraction of multiple attribute information of buildings in urban areas can be achieved by the U-net identification method, spatial overlay and kernel density estimation method, Kriging interpolation method, statistical analysis, and multi-temporal ground cover data analysis. In the future, this research method system can be used to provide some technical support to realize the establishment of an earthquake insurance exposure database, which can effectively obtain the building attribute information on a large scale. However, there are many problems in the research method of this paper:  
1. The extraction results of the building footprint are not fine enough.