## 4 Results  
For better understanding of the effectiveness of ELD, TD and CD methods, each was first analyzed separately and then three methods were combined. Due to the variability of window size in texture analysis, this method was first examined in five window sizes. The results showed that \(3\times 3\) window size had the best performance (Figure 7), so we chose it as the final result of this method according to damage states explained in Figure 8. It should be noted that among the 7 parameters of this method, mean and variance parameters provided the highest overall accuracy. In the next stage of analysis, the results of DSMs were extracted, which showed that this method has an ability to assess the damage so that it provided an overall accuracy of 78.1% for 4 DR state (Figure 9). In addition, in 3 DR state, the overall accuracy was 87.1%. In damage detection using remote sensing images that are viewed vertically, since direct information about the damage of columns, walls and internal components of the building is not available, the roof of the structure plays a key role in classifying the damage. In some cases, in moderately damaged buildings, residents may cover the roof of the building with a blue plastic tarp to prevent water penetration. This makes it difficult to detect moderate damage in blue tarp-covered ceilings in post-event images [15]. Also, by considering other factors that reduce the accuracy of the results, such as the horizontal difference in the images caused by landslides and the great imbalance in the three groups of damage, it can be said that the evaluation provided an acceptable accuracy) Figure 11). According to the experimental results of this study, if the number of response variables (in this study: the number of damage rates) is increased, the amount of training data should be increased accordingly. Otherwise, the algorithm will not have the correct analysis and estimation. In this study, due to the low number of buildings with a damage rate of D2 to D6,  
Figure 6: Location of training (**a**) and prediction (**b**) dataset of the study area. Green points (intact buildings), Blue points (low to medium damage) and Red points (Collapsed buildings) (3 DR). it was not possible to increase the number of training data. If the number of training data increased, there would be no data to estimate. For example, the number of structures in the collapsed group was 1128 structures. From these, 1000 structures were used for machine training and 128 structures for predicting. Basically, the number of training data in each group should be the same, so in the other groups, the number of this data was selected in proportion to the D6 group. Despite this fact, most of the time, after earthquakes or other catastrophes, there is no balance in the data and this problem must be solved in another way. On the other hand, having a large number of structures in the study area and multiplicity of variables reduce the efficiency of algorithms and the accuracy of the results. To reduce the impact of these problems, better results can be obtained by dividing large areas into several small areas and by performing several separate analyses. In the following part of the study, the pre-processed coherence data of pre- and post-event were subtracted from each other. After using machine learning algorithms, despite the significant advantage of this method, which has been emphasized in various researches, it resulted in very low accuracy. The results of CD method showed that this method is not able to detect minor to moderate damages. For example, in 3 DR case, by examining the results, it was found that the algorithm classified the majority of D2 data as underestimation in the almost intact group (Negligible to Slight damage) and categorized the majority of D4 data as overestimation in the Collapsed group. In addition, group D3 is divided between groups depending on the extent of damages. Figure 7: Texture analysis results considering 5 window sizes using RDF and SVM algorithms (3 DR).