# Building Damage Assessment Based on Siamese Hierarchical Transformer Framework
## 4 Discussion  
### Findings and Implications  
Pairs of pre-disaster and post-disaster satellite images can reflect the building damage level in the disaster-affected areas in a timely and accurate manner. Therefore, we construct the two-stage SDAFormer framework. Meanwhile, Swin Transformer is introduced to form the framework. According to the analysis of experimental results, our framework has a higher overall score than existing CNN-based methods, which proves the effectiveness of our method. The proposed two-stage framework can consider the temporal and spatial relevance between pre- and post-disaster remote sensing images, which helps to improve the building segmentation and damage assessment.  
The application of different types of Transformers in the visual field has been a hot research topic in recent years, which can improve the scalability and performance of many tasks. Transformer can correlate key features in different channels and improve the ability to model the global relationships of the framework in building damage assessment. In our study, Swin Transformer is applied in the encoder, decoder, and bottleneck, which shows the universality of the Swin Transformer block in the building damage assessment field.  
Figure 11: Results of the independent disaster events. First row to fourth row are: tornado in Arkansas and in Kentucky, typhoon on the Saipan Island and on the Tinian Island. (**a**) Pre-disaster; (**b**) post-disaster; (**c**) RescueNet; (**d**) MaskRCNN; (**e**) our results.  
In our study, a spatial attention mechanism, also known as the spatial fusion module, is also introduced. For the model with the spatial attention mechanism, the score of building localization is kept constant. Meanwhile, the accuracy of damage classification is improved, especially in the minor and major damage classes. The spatial fusion module has little impact on the semantic segmentation performance of the module, which can use the surrounding texture of the buildings to support damage-level inference. The spatial fusion module is able to enhance the spatial context feature representation and compensate for the limitation of window mechanism in Swin Transformer. As a result, the ambiguity caused by blurred post-disaster buildings can be alleviated, improving the ability to distinguish between minor damage and major damage level.  
According to Table 4, [PERSON]'s method (Mask R-CNN) has much lower \(F1_{loc}\) and \(F1_{cls}\) than SDAFormer. Unlike our proposed framework, in [PERSON]'s method, a single-branch decoder is applied after concatenating the extracted pre-disaster and post-disaster features. The single-branch decoder results in the inability to utilize the relevant features of the pre-disaster and post-disaster buildings. Therefore, buildings of different sizes are unable to be located properly, which affects the damage assessment result. Meanwhile, the CNN structure limits the global feature extraction capability and the overall understanding of the image. Regarding RescueNet, both pre-disaster and post-disaster images are simultaneously used for building segmentation, but only post-disaster images are used in the process of damage assessment. This strategy can provide additional information at different points in time to the segmentation task. However, since the structure of the buildings is damaged or destroyed by the disaster, the additional semantic information of the post-disaster images can lead to confusion of the network. Meanwhile, the damage classification task lacks the guidance on the localization of the original building, which weakens the ability to detect destroyed buildings because of the seriously damaged appearance.