## 3 Datasets and Preparations
### Bam Dataset  
On 26 December 2003, at 5:26 a.m. local time, a 6.5 magnitude earthquake, with its epicenter very close to the city and to the Earth's surface, hit Bam, Kerman, Iran. Approximately 27,000 deaths were recorded, 30,000 people were injured, and 75,000 people were left homeless. In terms of human loss, the quake was the worst in Iranian history [60,61]. About three months before the disaster, on September 30 th, Quickbird acquired an image from Bam with an off-nadir angle of 10 degrees. The post-disaster image was acquired eight days after the earthquake, on 3 January 2004, with an off-nadir angle of 24 degrees. The spatial resolution of the images was 2.4 m, which was then increased to 0.6 m through a pan-sharpening step. Building damages were mainly concentrated in the eastern parts of the city [42], while the western section of the city was covered mainly by vegetation and fewer buildings. Buildings were mostly built with mud bricks, and the overall arrangement of buildings in Bam was regular with low-rise, flat-roof buildings, and there was no separation between most of the houses. Pre- and post-disaster satellite images of Bam city were used to verify the robustness  
Figure 2: Geographical distribution and type of disasters in the xBD dataset. The corresponding number of building damage classes in each disaster is shown in pie charts. Each disaster is pinned on the map by a relative icon that shows its type. On the pie charts, green, light yellow, orange, and red show no-damage, minor-damage, major-damage, and destroyed classes, respectively. Black represents the unclassified buildings. and transferability of the proposed model in this study. Figure 3 shows the geographical location of Bam in Kerman, Iran, and the municipality regions along with our study area. In total, 22,318 building polygons existed in the city boundary, while our study area, which was captured from the densest portion of the city, contained 10,360 buildings. High-quality ground truth annotations were generated with the help of experts who visually interpreted high-resolution pre- and post-disaster images of Bam. Even in high-resolution images, it was difficult to understand the details of the damages from close-nadir-looking satellite images [29; 47]. Therefore, based on the previous studies [48; 62; 63; 64; 65], and since the visual interpretation of pre- and post-disaster images was complicated for multi-class labeling, we only considered two classes of damage for further steps. Additionally, the building vector map was employed to locate the buildings and decrease human error during interpretation. After careful labeling, 4997 and 2299 buildings were classified as no-damage and destroyed, respectively. The other 3064 buildings were left unclassified. Since the pre- and post-disaster images were acquired with different viewing angles and at different times of the year, resulting in different vegetation cover and unequal lighting conditions (different building shadow lengths and brightness) over the region, the manual labeling of the buildings was a complicated task. Furthermore, no other ground truth information except the building vector map was available for the study area. Figure 4  
Figure 3: Location of Bam city in Kerman province of Iran (**top left**), the municipality blocks along with the building footprints overlaid with the pre-disaster satellite image of the study region (**bottom left**), and the post-disaster satellite image covered by some sample regions showing damage (red) and no-damaged (green) buildings (**right**). shows the satellite image acquired before the earthquake and three zoomed regions with their pre-/post-disaster images, along with ground truth data. We tiled the satellite image of Bam into 36 patches of 1024 \(\times\) 1024 \(\times\) 3 pixels to be similar to the xBD dataset. In the ground truth generation procedure, the highest precision and effort were put into annotating buildings reliably and with suitable distribution over the entire study area.