## 3 Experiment and Analysis
### Ablation Experiments  
To validate the contribution of each module to the proposed method, detailed ablation experiments were conducted using the same data and experimental setup. Table 5 displays the results of the ablation experiments performed on the post-earthquake dataset, where B represents the BDAM module, M represents the MFAF module, D represents the DBA module, and C represents the combined loss function module. By adding the BDAM module, the MFAF module, the DBA, and combined loss function modules to the model, the present method achieves the highest overall accuracy relative to the baseline method. Specifically, when the MFAF module was added to the model, the average F1 score and IOU increased by 1.03% and 0.91%, respectively, compared with the baseline. In contrast, when the BDAM module was added to the model in this paper, the average F1 score and IOU increased by 1.71% and 1.80%, respectively. These results demonstrate that BDHE-Net offers tremendous benefits in the task of building damage assessment. The introduction of the MFAF module effectively integrates the integrity information of houses of different scales and enhances the ability to identify the integrity of houses of different scales. The BDAM module, on the other hand, fully considers different collapse directions and texture characteristics of damaged buildings, enhancing model performance for extracting damaged houses. In addition, when the DBA module and the combined loss function module were added to the model in this experiment, the average F1 and IOU scores increased by 0.67% and 0.51%, respectively. This suggests that the DBA module plays an active role in model training. To rectify the disparity in sample distribution among different classes of building damage in the dataset, the DBA module incorporates a strategy that involves augmenting the dataset with a smaller number of samples (e.g., in the severe damage and collapse categories) while simultaneously expanding the damaged house regions. By introducing more samples of fewer categories and expanding the damaged areas, the model is able to learn and classify the small number of samples better, which improves the refinement of building damage assessment. The combined loss function combines Dice and focal losses, which can balance the weights of different category samples during the training process. This approach prevents the model from excessively concentrating on samples with a large number of categories, such as the intact category, while neglecting those with fewer instances. By adjusting the weights of the samples, the combined loss function ensures the model allocates more attention to samples that are challenging to classify, thereby enhancing the performance of building damage assessment. The results suggest that BDHE-Net shows significant advantages in the task of building damage assessment. This is because the BDAM module fully considers the characteristics of damaged buildings, e.g., different collapse directions and texture characteristics. Secondly, the DBA and combined loss function modules improve the model weight bias towards cases with a large number of categories. Finally, the MFAF module effectively fuses the house dimension information at different scales and enhances the integrity of houses at different scales. In summary, BDHE-Net demonstrates superior capabilities in conducting building damage assessments across multiple-resolution datasets.