## 3 Experiment and Analysis
### Comparative Analysis of Splitting Performance  
In this paper, three classic semantic segmentation models including DeepLabv3+, ResNet-50 [31], and U-Net [32] are used for comparison with our proposed model. The comparison results for the satellite remote sensing images from the Afghanistan earthquake are presented in Figure 6, while those for the UAV images from the Baoxing earthquake are displayed in Figure 7. The figure reveals an incorrect categorization of damaged houses by the U-Net model. The ResNet-50 model fails to provide clear boundary information for different building categories. In addition, the DeepLabv3+ model does not predict the outline of houses completely. In contrast, the BDHE-Net model proposed in this paper not only comprehensively extracts the contours of buildings but also effectively differentiates between buildings with different damage classes. To further assess the model's performance, a detailed analysis is presented in Figure 8 with satellite images and UAV images. The first row is the satellite remote sensing image, and the original image, zoomed in the red box, is shown in the second column. The main body of the house within the red box belongs to the slightly damaged category. However, the U-Net model misidentifies it as severely damaged. The ResNet-50 model struggles to differentiate between slight and severely damaged, categorizing it as a mix of both. The Deeplabv3+ model classifies it as severely damaged and fails to recognize the complete and regular shape of the main building. In contrast, our BDHE-Net model accurately identifies it as slight damage and recognizes the complete and regular shape of the main building. Figure 5: Training loss curves. Figure 6: Comparison of different segmentation models for the Afghanistan earthquake. Figure 7: Comparison of different segmentation models for the Baoxing earthquake. Similarly, the second row of Figure 8 contains a UAV image. The local image within the red box is enlarged and displayed in the second column. The left side of the building within the red box corresponds to the collapsed category, while the right side corresponds to the severely damaged category. It is evident that both the U-Net and ResNet-50 models predominantly classify the houses on the left and right sides as severely damaged, failing to differentiate between collapsed and severely damaged structures. The Deeplabv3+ model fails to recognize the complete and regular shape of the building. However, our BDHE-Net model accurately identifies the left side as collapsed and the right side as severely damaged while recognizing the complete and regular shape of the building. This demonstrates the effectiveness of our model in predicting building damage level classification. Table 4 presents a quantitative comparison of building damage classification accuracy among various baseline models in our post-earthquake dataset. As indicated by the experimental results in Table 4, the proposed BDHE-Net surpasses the other models, achieving an average F1 score of 66.35% and an IOU of 47.15%. Compared with U-Net, ResNet-50, and DeepLabv3+, the average F1 score of BDHE-Net improved by 6.57%, 6.19%, and 8.22%, respectively, and the average IOU increased by 6.62%, 6.24%, and 8.09%, respectively. For the intact category, there was a slight difference in F1 between the four models, indicating that all models performed well in distinguishing intact buildings. However, for the severely damaged categories, BDHE-Net improved its F1 scores by 3.62% to 6.81% compared with the other models. This result proves the effectiveness of our proposed DAM module and combined loss function; by combining the method, BDHE-Net effectively improves the original model weights biased towards categories with the high number of categories, which results in good accuracy of the model in the lesser number of slightly damaged and severely damaged categories.