## 6 Discussion
Therefore, it is important to understand this limitation and its impact on the analysis and results; thus, it should be considered when interpreting the findings. We exclusively utilized the pre-disaster images to analyze the effect of parameters on the localization stage performance. The effect of parameters on the quality of localization results was studied in seven different configurations which are illustrated in Figure 21. The results illustrated that experiment A (images with 1.2 m < GSDs \(\leq\) 1.8) achieved the best  
Figure 20: Distribution of influencing parameters and the thresholds specified for conducting each experiment. The number of images for each experiment is presented next to the curly brackets. Subfigures are displaying (**a**) histogram of pre-disaster Sun elevation angles, (**b**) off-nadir angle vs. ground resolution, (**c**) histogram of differences between pre- and post-disaster Sun elevation angles, (**d**) histogram of differences between pre- and post-disaster GSDs, (**e**) histogram of differences between pre- and post-disaster off-nadir angles. The blue circles are showing the specified threshold which has been used for further analysis. performance. This highlights the importance of using high-resolution images for building footprint localization. Conversely, the worst performance was obtained when utilizing close-to-nadir images (images with off-nadir angle \(\leq 18^{\circ}\)) in experiment C. The large difference between values obtained from experiments C and D suggests that compared to nadir images, oblique satellite images could generate more accurate building masks. Experiments F and G, with a subtle difference in their performance, show that shorter shadows (images with Sun elevation \(>50^{\circ}\)) obtain better masks. In order to study the effect of relative parameters on the classification stage performance, we should have used both the pre- and post-disaster images. In these experiments, we investigated the relative relation between pre- and post-disaster satellite images, such as the differences in Sun elevation angle, differences in GSD, or differences in off-nadir angles. Figure 22 illustrates the evaluation metrics computed from each experiment. Although the results have shown approximately the same performance in all configurations, satellite images with more similar Sun elevation angles could result in higher accuracy. Finally, we investigated the model's performance based on individual natural disaster types. Accordingly, images from different events such as fires, earthquakes, volcanic eruptions, tsunamis, or winds were separately fed into the model, and the evaluation metrics were calculated (see Figure 23). Fire-related images obtained the best performance when considering all the evaluation metrics simultaneously. One reason for this achievement is that damaged buildings in fire-related images are mostly destroyed, and their damage assessment was performed more confidently. Volcanic eruption events had the least number of images (only five images), and their evaluation was not sta  
Figure 21: The relation between different parameters and the “localization” quality metrics. Figure 22: The relation between different relative parameters and the “classification” quality metrics. events had experienced the worst performance in damage assessment and classification. One reason for this behavior could be the types of damage that occur to buildings after typhoons and tornados, which are different from other disasters. According to our experiments, several key factors can critically affect the performance of deep learning semantic segmentation networks for building damage assessment. Either the data imbalance towards specific classes or specific disaster types, such as the case in the xBD dataset, can decrease the training performance. Although the architecture and depth of deep networks are important parameters, other hyperparameters such as loss function, activations, or attention modules play major roles in model performance.