## 2 Related Works
Compared with other architectures, UNets, with encoder-decoder paths that were improved by adding skip-connections to aggregate low-level and high-level features, have been widely used for the task of building damage assessment [12; 36; 46]. In order to improve the performance of baseline semantic segmentation architectures, researchers have used various strategies and extensions in their networks. For instance, [PERSON] et al. [52] used a lightweight approach based on knowledge distillation to reduce the dependence on computing resources and increase the speed of damage assessment in emergency situations. In contrast, in order to achieve more accurate results with fewerlabels, [PERSON] et al. [53] implemented a weakly supervised technique that could improve the quality of activation maps and boost model performance. Attention techniques [34; 53], which try to emphasize important regions of the images, have been widely used in computer vision tasks. [PERSON] et al. [34] used a difference-aware attention strategy to predict more accurate localization and classification results after disasters. For a superior combination of features in spatial and channel directions, [PERSON] et al. [36] proposed BDANet with a cross-directional attention mechanism and obtained enhanced damage assessment maps. Attention-based mechanisms have shown promising results in both computer vision and building damage assessment. From the development point of view, appropriate and carefully annotated datasets play a major role in deep learning models. Deep learning models are data-hungry, and the issue is exacerbated by the complexity of the problem [54]. Building damage assessment is a complicated problem and, thus, requires large amounts of ground truth data for model development. Preparing annotated remote sensing images for such tasks is time-consuming and requires huge manual work. Although the number of benchmark datasets is growing in the remote sensing community, high-quality datasets that are suitable for building damage classification are still rare [55]. The xBD dataset [56] has played a game-changing role in building damage assessment during the past five years. However, it still has some challenges, such as highly biased classes towards no-damage buildings. Furthermore, complexities of minor-damage and major-damage classes, besides similarities between minor-damage and no-damage classes, lead to moderate misclassification [57]. Preprocessing techniques such as data augmentation have been widely utilized to increase the size of data and robustness of the model [58]. They can also act like regularization techniques that feed models with more challenging scenarios [58; 59]. Advanced augmentation methods, such as the Cutout, randomly mask out square regions of input images to improve the feature representation ability of the model [41]. Currently, most of the deep learning architectures for building damage assessment follow the concepts from change detection problems. The main framework seems to be the two-stage model with separate localization and classification streams. Moreover, obtaining accurate results for challenging classes is still an open problem. A two-stage UNet-based network is proposed and enhanced with SKM to improve feature representations and deal with previous limitations. Accordingly, augmentation techniques were applied to cope with the problem of imbalanced datasets to alleviate the complexities of damage classification.