## 2 Related Works  
This section discusses the methods that recently were used for building damage assessment using remote sensing satellite images. Due to the high impact of natural disasters on human lives, it has always been a hot topic to study various techniques to cope with the damages that occur to cities. Many researchers from two decades ago have started to use satellite images for the purpose of building damage assessment [6; 20; 42]. In the meantime, researchers have developed more sophisticated methods based on machine learning techniques to automate the assessment process [9; 25; 43]. As an example, [PERSON] et al. [25] tried to generate building damage assessment maps using textural and spectral features extracted from pre- and post-disaster Sentinel-1 and Sentinel-2 images. They fed the features into a Random Forest classifier for classification. [PERSON] et al. [44] compared different morphological profiles with [PERSON]'s texture features to detect damaged buildings using a k-Nearest Neighbor classifier. Likewise, [PERSON] et al. [45] utilized an ANFIS-based decision-making system powered by geometrical features and bi-temporal satellite images combined with building vector maps to detect damaged buildings in the Bam earthquake. Recently, deep learning techniques have been widely studied to provide more general solutions for the task of building damage assessment [19; 36]. The ability of deep learning models to provide high-dimensional representations of images has enabled them to solve more sophisticated challenges. For instance, [PERSON] et al. utilized a deep object-based semantic change detection method to evaluate damages to buildings that occurred due to natural and man-made disasters [19]. In [46], [PERSON] et al. proposed a variety of attention UNets to localize and classify building damages. By detecting changes in super-pixels, [PERSON] et al. [14] proposed a CNN-based network for damage assessment using UAV images. Moreover, in [12], [PERSON] et al. proposed a two-stage UNet supplemented with the Squeeze and Excitation (SE) module to improve the damage assessment results. According to the usage of only post-disaster images [11; 32; 47] or the pair of pre- and post-disaster images [12; 34; 36], various methodologies have been proposed for building damage assessment. In [29], [PERSON] et al. proposed various fusion strategies for building damage assessment from post-disaster satellite, aerial, or UAV images using CNNs. In [15], [PERSON] et al. proposed an unsupervised method for building damage assessment. However, without any guidance from pre-disaster images or possible external sources of building footprint maps such as OpenStreetMap [15], post-disaster images cannot provide precise building boundaries [14]. In this regard, in 2013, [PERSON] et al. [9] showed that most of the studies tend to use pairs of pre- and post-disaster images to be able to locate the damaged building and evaluate the damage by detecting meaningful changes. Accordingly, [PERSON] et al. [48] extracted building properties such as shape, geometry, shadow, texture, and color from pre- and post-disaster imagery and fed them into machine learning algorithms for improved building damage assessment. Likewise, [PERSON] et al. [49] compared different pre- and post-disaster feature fusion scenarios in CNN models for the Haiti earthquake. From the architecture's point of view in deep learning methodologies, various architectures from the computer vision community have been proposed for building damage assessment. Inspired by atrous spatial pyramid pooling networks, with dilated convolutions, [PERSON] et al. [50] proposed a network (RescueNet) with multiple segmentation heads for simultaneous building detection and damage classification. [PERSON] et al. [51] studied the effective parameters of damage assessment predictions by using simple, fully connected CNNs.