# Building Damage Assessment Based on Siamese Hierarchical Transformer Framework
## 3 Experiment Results
### Ablation Study  
An ablation experiment is conducted to demonstrate the effectiveness of our proposed method. To investigate the effectiveness of the Transformer-based structure, a CNN-based network using the backbone of Res-50 without additional modules as the baseline is  
Figure 6: Building damage assessment results. (**a,b**) respectively show pre- and post-disaster images; (**c**) shows ground truth; (**d,e**) are the results of RescueNet and MaskRCNN, respectively; (**f**) is the prediction of our proposed framework. Figure 7: Visual examples of segmentation results in Stage 1. (**a**) shows pre-disaster images; (**b**) shows ground truth; (**c,d**) are the results of RescueNet and MaskRCNN, respectively; (**e**) is the segmentation result of our proposed framework. implemented for comparison. To evaluate the contribution of the SF module, a Transformer-based network without the SF module is used. Table 5 shows that the scores of Transformer-based methods exceed the score of the Res-50-based baseline, which proves the effectiveness of the Transformer-based network. Furthermore, SDAFormer achieves an impressive improvement with the SF module. It can be seen that the \(F1\) score of damage classification (\(F1_{cls}\)) is improved to 77.6%. The result shows that the SF module has little influence on the localization accuracy, but obvious performance gain is achieved on the classification accuracy. The score of minor damage class (\(F1_{C_{2}}\)) obtains a better improvement (from 57.0% to 61.4%), and it shows that the spatial attention mechanism helps enhance the performance on the damage level, which is difficult to recognize. To further evaluate the influence of the Transformer structure and SF module, three groups of sample images in the test set are picked out for comparison, as shown in Figures 8-10, respectively. Figure 8 illustrates the performance on the detection of undamaged and minor damaged buildings. It can be seen that SDAFormer has more prediction mistakes on minor damage. There are several reasons for this. Firstly, the training set is imbalanced on the damage level annotations and heavily biased toward the level of no damage. Secondly, the high visual similarity between no damage and minor damage leads to the misclassification of these two classes. Moreover, as shown in Figure 8a,b, the imaging angles of the pre-disaster and post-disaster images are different, which leads to an incomplete overlap of building locations in the two images. Figure 9 illustrates the performance on the detection of undamaged and major damaged buildings. It can be seen that the Transformer-based models perform well in detecting undamaged buildings. However, due to the complex damage distribution of the local buildings, wrong judgments are made for the major damaged buildings. The baseline method achieves a lower prediction accuracy, where some undamaged buildings are incorrectly classified as major damaged buildings. In comparison with the result of the SDAFormer without the SF module, the SDAFormer with SF module outputs more accurate results on the major damage classification. Figure 10 illustrates the performance on the detection of destroyed buildings. The assessment result shows that the output of all models is generally correct in terms of building localization. As for the damage assessment, the baseline output in Figure 10d has a few classification errors in the lower left corner of the image, and the quality of the assessment for small building objects is not satisfactory. The output of SDAFormer can indicate the damage degree of the disaster-affected area, but the results differ in the details. For example, the building group in the center of Figure 10e is regarded as major damage by SDAFormer without SF. In Figure 10f, the building group is regarded as undamaged except for the lower right corner.