## 6 Conclusions  
In this study, a combination of SAR and LIDAR images was used for evaluation. The CD parameter in pre- and co-event SAR images and the TD (in five window sizes) and the ELD parameters in pre- and post-event LIDAR images were analyzed. To classify and predict the results, machine learning-based algorithms, including RDF and SVM were used. In the first step, the LIDAR images were preprocessed and pre- and post-event DSMs were extracted. Then, the variation between the two images was calculated by simple differentiation. In the second step, the seven second-order texture parameters (Mean, Variance, Homogeneity, Dissimilarity, Contrast, Entropy and Correlation) in 5 window sizes (\(3\times 3\), \(5\times 5\), \(7\times 7\), \(9\times 9\), \(11\times 11\)) were calculated separately for pre- and post-event images. Then, the differences of the extracted parameters in the two images were computed. In the third step, after analyzing the SAR data and creating coherence images, the difference between both images was calculated. In order to estimate and categorize the results, machine-learning algorithms including RDF and SVM were used. Part of the data was allocated for algorithms training and the rest of the data was used to predict the rate of damages. This study was performed in two cases: in the first case, 3 damage rates and in the second case, 4 damage rates were considered. The SVM algorithm using the ELD parameter and considering three damage rates provided an overall accuracy of about 87.1%. In addition, in 4 damage rates, the overall accuracy was about 78.1%. The results showed that methods based on LIDAR images are more efficient. Regarding the methods used to analyze these images, we conclude that:  
1. In the future, the LED method can be a good alternative to field research, which is very time consuming and costly. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No** & **Research** & **Type of Data** & **Algorithm** & **Accuracy** & **Event** \\ \hline
1 & [PERSON] et al. [5] & LIDAR & SVM & 92\% & Kumamoto earthquake \\ \hline
2 & [PERSON] et al. [37] & LIDAR & SVM & 91.59\% & Haiti earthquake \\ \hline
3 & [PERSON] and [PERSON] [38] & LIDAR & SVM & 78.90\% & Haiti earthquake \\ \hline
4 & [PERSON] et al. [39] & LIDAR & SVM & 78.8\% & Lorca earthquake \\ \hline
5 & [PERSON] [40] & LIDAR & SVM & 80\% & Kobe earthquake \\ \hline
6 & [PERSON] et al. [35] & Landsat, ALOS & SVM & 98\% & Cambodia forests \\  & and LIDAR & RDF & 66\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Some examples of research done with RDF and SVM algorithms using LIDAR images. Among seven texture properties mentioned in the previous sections, mean and variance played a more effective role in the results. According to the results from this method, it can be considered as a complement to other methods. In a separate experiment with two damage rates, the overall accuracy of this method increased about 10%. The CD method based on SAR data provided poor results in identifying the three damage rates. In evaluating the two groups of damage, including intact buildings (D0, D1, D2) and collapsed group (D3, D4, D5, D6), the overall accuracy of this method increased to about 60%. Another factor that could increase the accuracy of this method about 8% was the division of study areas, which made it easier to make algorithms decisions. In general, it can be said that this method cannot still be replaced by field study because it has low ability to evaluate buildings with moderate to low damage. Regarding the current capability of machine learning algorithms, for future work, it is recommended that building damage assessments be performed in several separate sections with less data, rather than an integrated analysis for a large area. In addition, the effects of landslides on reducing the accuracy of the results can be investigated. Conceptualization, S.K. ; M.M. and M.H. ; methodology, S.K. and M.H. ; software, M.H. and S.K. ; validation, M.H. and S.K. ; formal analysis, M.H. and S.K. ; investigation, M.H. ; S.K.