## 6 Discussion
For each disaster event, the number of image pairs (top right), type of disaster (bottom right), and pre-/post-disaster sun and satellite angles are visualized in each box. Focusing on satellite look angles before disasters (the blue satellites) reveals that most of the images were taken from near-nadir angles, which results in sharper images, while in post-disaster conditions (red satellites), images were mostly taken during emergency situations that satellite tracks were not necessarily crossing the region of interest and look angles tend to be higher. Another aspect of image attributes in the xBD dataset is the time gap between pre- and post-disaster image acquisitions, which results in different sun elevation angles (the red and blue Sun symbols). This will result in dissimilar shadow lengths and directions, which could affect the inference procedure. Moreover, in order to show that ground sampling distance (GSD) was not consistent across images in xBD, two scales are visualized on the top left corner of each box. A 1 m\({}^{2}\) GSD is in the middle, and the larger squares relatively show how much the actual GSDs in each imaging scenario vary concerning the reference scale. The closer the boxes are to the reference central box, the less variation exists in the imagery resolutions. Furthermore, different box sizes in pre- and post-disaster cases explain the complexity of GSD variations in the xBD dataset. This scale simplifies the understanding of different resolutions in pre- and post-disaster situations. For the first time, Figure 19 provides a detailed representation of the xBD dataset, revealing various associate imbalance attributes, including different Sun elevations, off-nadir-looking angles, and dissimilar GSDs in pre- and post-disaster images. One of the key contributions of this study was that we investigated the correlations between these attributes and the performance of our proposed model in detail. Accordingly, multiple experiments were designed to analyze how each parameter could affect the performance of our proposed model in either the localization or classification stages. To address this investigation, we identified three parameters of (1) Sun elevation angle, (2) GSD, and (3) satellite off-nadir looking angle, as the influencing parameters on the localization stage performance. Similarly, three relative parameters between pre- and post-disaster images, including (1) differences in Sun elevation angle, (2) differences in GSDs, and (3) differences in off-nadir angle, along with the disaster types, shaped the structure of four influencing parameters on the classification results. In order to be able to conduct these experiments, we should have chosen some thresholds for each of the influencing parameters to create a comparison basis and assess the performance of the model. In this regard, we investigated the distribution of each variable by studying its histogram. Figure 19: Visualizing imaging properties in 19 disaster events of the xBD dataset, before and after the disasters. Figure 20 illustrates the histogram of the above-mentioned variables. Since image resolution and off-nadir-looking angle have a physical relationship with each other, a bi-variable scatter plot was generated for further investigations. With the aim of comparing the localization and classification results, we chose thresholds that make distinctive separations between the variables' values. For instance, we decided on 50\({}^{\circ}\) as the separation value for the pre-disaster Sun elevation angles, making an approximate 10\({}^{\circ}\) separation between the two groups so that their comparison would be more reasonable and meaningful. Moreover, on top of each graph, the number of images that fall within the threshold limits is illustrated. We only employed Test set images with more than one building for these experiments, which resulted in 752 image pairs. It is important to recognize that the varying number of images in each experiment was a factor beyond our control.