## 5 Experiments and Results
### Transferability Analysis  
Given the emergency disaster context, accessing real-time and appropriate annotated images from the current disaster events to train or fine-tune deep learning models is not always possible. Eventually, accurate annotation of images, even with a team of experts, is time-consuming and cannot be performed within a short time just after the disaster. In such conditions, a pre-trained model that is already trained on images from past disasters could be capable of generalizing to the images of the new disaster [79] with no or less training effort. Here, the difficulty and complexity of the generalization problem lie in the variations between either of the datasets (i.e., images of the current disaster and those of previous disasters). Major sources of such variations could be the change of geographical region (e.g., differences in textures and urban structure) or acquisition attributes (e.g., differences in Sun elevation angle, satellite-looking angle, and acquisition time), which can result in different types of images compared to those that the predefined model was trained on. To further study the robustness, generalization power, and transferability of our proposed method, we used another set of data, i.e., Bam, Iran earthquake images, which  
Figure 17: Visual comparison of damage assessment results from four models used in our study. were not included in the xBD dataset. The results of applying our proposed model to the Bam city dataset are presented in this section. Differences between the Bam earthquake dataset and xBD are threefold:  
1. Their acquisition conditions, i.e., time, geographical region, and satellites, are completely different. The xBD images were mostly acquired with Worldview-2 and -3 satellites from 2018 to 2021, while the Bam images were captured by Quickbird in 2003. Moreover, no instance of disaster events from Middle-Eastern countries exists in the xBD dataset. 2. Building types in Bam differ from those in xBD, with most of them being low-rise and compact, which were built in a dense tree-covered region;
3. There are almost no instances of earthquake-damaged buildings in the xBD dataset, except for only three damaged buildings in the Mexico earthquake, (see Figure 2), meaning that our models were completely trained on natural disasters other than earthquakes. Several previous studies concentrated on the binary distinction of building damages. Moreover, binary classification often satisfies the requirements of operational cases in emergency situations [51]. On the other hand, it is essential to note that visual assessment of building damages and assigning damage categories on a 4-level scale from only satellite images is an uncertain and complex task. Minor- and major-damage classes are difficult to assess. Only those buildings with some destruction on their rooftops or damages distinguishable from surrounding debris can be confidently classified. Thus, we used a limited number of classes for the Bam scenario. Accordingly, minor- and major-damage predictions from classifiers were ignored, and a binary classification map was generated for the study area, including only no-damage and destroyed classes. Based on the pre-disaster images, almost all of the buildings were identified correctly in the localization stage, except for a limited number of false positive predictions due to the shape of buildings or the overlap of trees. For the task of damage assessment, based on the pre- and post-disaster satellite images, damage classes were identified precisely. The overall accuracy was 84%, and F1-score values for no-damage and destroyed classes were 80% and 87%, respectively, showing promising results for transferring damage assessment learning from previous disaster events to a new and unseen dataset. Figure 18 demonstrates a combination of localization and damage assessment results on seven sample regions of Bam city, along with their probability maps derived from the proposed model.