## 6 Discussion  
This paper focused on designing and enhancing a two-stage UNet-based building damage assessment network to achieve accurate and precise building localization and damage classification, which led to at least a 5% increase in the evaluation metrics. Pretrained backbone feature extractors were utilized to extract deep representations from pre- and post-disaster satellite images. Simultaneously, the SKM, conceptualized from the idea of adaptive receptive fields, was introduced into the UNet architecture to enhance the performance of the model. Fair comparisons, in either of the localization and damage assessment stages, were made between the proposed model and state-of-the-art UNet-based architectures. Experimental analysis showed that the proposed framework outperformed existing CNN-based methods, proving its effectiveness. In the first stage, as illustrated in Figure 14, the localization results obtained from the proposed model outperformed other models in terms of all the evaluation metrics. The proposed model resulted in fewer false positive (false alarms) and fewer false negative pixels. In addition to large buildings, which are simpler for identification, sparse and smaller buildings in non-urban regions or compact buildings in dense urban regions were also correctly located. The second stage, which was conceptualized based on change detection approaches, used a Siamese-based two-branch UNet architecture. The SKM was also utilized in this stage to improve damage classification results. Training and validation metrics, along with visual investigations, demonstrated the superior performance of the proposed model with respect to other methods. It was observed that although the models successfully identified  
Figure 18: Building damage assessment transferability results for seven regions in the Bam earthquake dataset (**a**–**g**). Pre- and post-disaster images are shown on the left. Probability maps for no-damage and destroyed classes are shown in the middle with the relevant color map. The building damage classification map is demonstrated on the right, and the ground truth data for each building is overlaid with green or red borders around it. damaged portions of buildings, they were not accurate in assigning them to the relevant classes. This mostly happened in buildings with minor- or major-damages classes where some part of the building is usually damaged. This finding highlights a limitation of the current models in handling complex damage prediction tasks and suggests areas for future research and development. It should be noted that the classification of all four damage categories was difficult, leading to misclassifications in minor- and major-damage classes compared to no-damage or destroyed classes, which were mostly classified correctly. This behavior can be attributed to several reasons. Firstly, there was a significant imbalance in the number of samples between damage categories. Secondly, other sources of uncertainty and complexity in the dataset, such as relying solely on satellite images, could have contributed to the misclassification issue. It is worth mentioning that even with high-quality images, evaluation of damage categories for buildings using only satellite images is a complicated task, as the assessment is limited to the building's roof, while some categories of damage in natural disasters affect walls and building floors. The addition of extra degradation parameters such as cloud coverage, varying viewing angles, as well as changes in sun elevation angle increase the level of difficulty in the damage classification procedure. In this regard, for a better understanding of these sources of uncertainty, Figure 19 visualizes the image acquisition conditions in pre- and post-disaster images for all 19 disaster events in the xBD dataset. This has been conducted by schematically demonstrating the sun and satellite positions, as well as the variations in spatial resolution, before and after each disaster.