# Building Damage Assessment Based on Siamese Hierarchical Transformer Framework
## 3 Experiment Results
### Comparisons with Other Models on xBD Dataset  
To verify the effectiveness of the proposed method in this paper, we compare it with some existing CNN-based methods, including* The [PERSON]'s method [53] utilizes Mask R-CNN with FPN structure and parallel architecture for both building segmentation and damage classification. It concatenates pre-disaster and post-disaster features after feature extraction with ResNet-50. Then, the fused feature map is fed into the segmentation head for damage assessment. A novel loss function is designed to weight the mistakes on levels inversely proportional to their occurrence in the xBD dataset. * In RescueNet [54], a dilated ResNet-50 is used for the backbone of the U-Net. To utilize the differences between pre-disaster and post-disaster images, both images are fed into the network for building segmentation. Only the post-disaster image is used in the task of damage classification. Different loss functions are applied to the two tasks separately. Specifically, the Binary Cross-Entropy loss is used for building segmentation, while the foreground-only selective Categorical Cross-Entropy loss is used for damage classification. A dual-head framework is developed, which contains a segmentation head and a change detection head. * The approaches of the top two results from the XView2 Challenge are employed for evaluation, including XView2 1 st [55] and XView2 2 nd [56]. The XView2 1 st builds a multi-model ensemble for better performance. XView2 2 nd simultaneously applies DPN-92 and DenseNet-161 to U-Net for damage assessment. Both methods use various techniques including data argumentation and multiple test strategies. Table 4 shows the damage assessment results of different methods on the xBD dataset. In the overall task, our framework performs better and reaches the score of 80.2%. In comparison with the highest overall score of XView2 1 st, our method obtains a 1.5% boost. The \(F1_{loc}\) is improved compared with MaskRCNN and RescueNet. The \(F1_{C_{i}}\) scores also reach their highest, except that the \(F1_{C_{1}}\) is slightly lower than the method of XView2 2 nd, which may be due to the dual-model strategy of XView2 2 nd. Figure 6 visualizes the damage assessment results of each method. For RescueNet, more errors appear in the damage classification, especially on the minor damage level. For Mask R-CNN, it can be seen that the model outputs more segmentation mistakes, and more mistakes appear on the edges of damaged areas. It can be seen that our proposed model obtains more accurate damage level prediction with smoother boundaries. Overall, the proposed SDAFormer performs best with fewer assessment mistakes. Figure 7 visualizes the results of the building segmentation, which is Stage 1 of the proposed framework. The results of Figure 7c,d mistakenly detect the area of cropland as buildings and cannot provide accurate contours of the buildings in the pre-disaster image. It can be seen that our framework achieves more precise segmentation results in comparison with the other methods. In the results of our method, the contours are clear enough for the siamese network to locate the buildings. Thus, the output of Stage 1 is adequate for locating buildings in Stage 2 where damage classification is to be performed. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Methods** & **Score (\%)** & \(F1_{Loc}\) (\%)** & \(F1_{cls}\) (\%) & \(F1_{C_{1}}\) (\%) & \(F1_{C_{2}}\) (\%) & \(F1_{C_{3}}\) (\%) & \(F1_{C_{4}}\) (\%) \\ \hline MaskRCNN [53] & 74.1 & 83.6 & 70.0 & 90.6 & 49.3 & 72.2 & 83.7 \\ RescueNet [54] & 77.0 & 84.0 & 74.0 & 88.3 & 56.3 & 77.1 & 80.8 \\ XView2 1 st & 78.7 & 86.1 & 75.5 & 91.9 & 57.2 & 77.0 & 86.3 \\ XView2 2 nd & 76.8 & 84.0 & 73.7 & **92.8** & 53.8 & 75.2 & 85.9 \\ \hline SDAFormer & **80.2** & **86.1** & **77.6** & 92.5 & **61.4** & **77.5** & **86.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of the proposed method with existing CNN-based methods.