## 1 Introduction  
Annually, thousands of people worldwide lose their lives due to damages caused by natural disasters [1; 2; 3]. Countries spend millions of dollars on rebuilding infrastructures and compensating for the associated damages. Accordingly, providing accurate building damage assessment maps, with the location, number, and severity of damages, is critical for emergency responders and relevant organizations to manage their resources appropriately [4]. Remote sensing images are capable of providing overviews from large regions of the affected area for faster building damage assessment. Remote sensing data, i.e., satellite images, drone/aerial imagery, or Synthetic Aperture Radar (SAR) data, have proven their applicability in providing useful information for disaster management. Although SAR sensors propose all-weather data availability [5; 6], or drone imagery obtains higher spatial resolutions [7; 8], satellite optical images are more cost-effective with respect to the coverage area. They can be obtained in near-real-time thanks to the numerous satellites and constellations in orbit, and thus, are one of the best choices to rely on during disaster conditions [9]. In summary, key attributes of optical satellite images are their wide area coverage, rapid revisits, simple interpretation, and fast deployment [9]. According to the temporal usage of satellite images for building damage assessment, the current literature can be divided into two categories: (1) techniques that only use post-disaster images and (2) techniques that are based on both pre- and post-disaster images. For the first group, which relies on post-disaster images, the task of damage assessment is mainly considered to be a semantic segmentation problem [10; 11; 12; 13]. However, without using pre-disaster images, post-disaster images are not appropriate sources for precise extraction of building footprints [14]. Additionally, researchers require auxiliary information, such as pre-disaster building masks obtained from external resources, e.g., municipality databases or OpenStreetMap, to enhance their results [15; 16]. On the other hand, having both pre- and post-disaster satellite images from the affected region will help locate the buildings and assess their damaged parts. It is worth mentioning that the problem of building damage assessment using both pre- and post-disaster satellite images is inherently very similar to a change detection problem. In both scenarios, pre- and post-disaster images are compared with each other to find changes [17; 18]. The main difference, making damage assessment a more complicated problem, is that in change detection, only changed objects (e.g., buildings) are detected, and everything else is ignored. In contrast, both changed (damaged) and unchanged (not-damaged) buildings should be found in the building damage assessment. Another difference is the requirement to classify different damage categories, which implies a multi-class change detection problem [14; 19]. From another point of view, according to the utilized processing techniques, building damage assessment has been addressed by (1) visual interpretation of images, (2) machine learning-based methods relying on hand-crafted features, and (3) deep learning techniques. Traditionally, satellite images were visually interpreted by researchers and experts to obtain estimations of the amount of damage over the affected area [20]. Even with large groups of experts and interpreters, visual investigation of remote sensing images for building damage assessment is not a time- and cost-efficient procedure. Therefore, automatic techniques were introduced to process remote sensing images for building extraction and damage assessment. Based on the human experience obtained from visual interpretations, machine learning techniques with very few trainable parameters, which relied on hand-crafted features, such as textural, spectral, and spatial features, were developed [21; 22; 23; 24].