## 1 Introduction
In addition to the amount of time and experience required for feature extraction, manually extracted features were not generalizable to other geographical regions, and in most cases, they were valid for specific conditions [25]. Recently, deep learning models, primarily based on Convolutional Neural Networks (CNNs), have shown great success in various computer vision tasks such as classification or semantic segmentation [26; 27; 28]. These models are well-suited for such applications due to their ability to automatically learn hierarchical representations of low- to high-level features from raw images. Many studies leveraged deep learning methods to tackle the task of building damage assessment and achieved noteworthy developments [15; 29; 30; 31]. For instance, [PERSON] et al. [32] classified building patches into four levels of damage by employing CNNs on post-hurricane Unmanned Aerial Vehicle (UAV) images. Likewise, [PERSON] et al. [33] used bi-temporal images acquired before and after disasters to develop an end-to-end procedure for solving a semantic segmentation problem with the aim of building damage assessment. Using pre- and post-disaster images is an approved way for building damage assessment. The key question is how to exploit the required information from these images to ensure efficient performance. Inspired by the Siamese-network concepts introduced by [PERSON] et al. and [PERSON] et al. in [17; 18], two-branch CNN architectures were used in building damage assessment using bi-temporal satellite images. Moreover, researchers utilized various types of feature fusion schemes to address this problem. For instance, [PERSON] et al. [30] conducted experiments on multiple feature concatenation strategies for building facade damage detection. In another study, [PERSON] et al. [34] developed a dual-temporal aggregation module to capture global change patterns and performed a difference-aware fusion technique to assess building damages. Based on the powerful properties of attention-mechanism-enabled networks, which focus on more important regions of the images [35], [PERSON] et al. [36] introduced a cross-directional attention module to explore the spatial and channel-wise correlations between pre- and post-disaster images. Based on the literature, our proposed method was motivated by the two-branch CNN architecture for pre- and post-disaster feature fusion. The basic foundation of our model relied on the well-known UNet model [37], widely used for segmentation tasks, which is an encoder-decoder-based network with skip-connections between the encoder and decoder paths for further enhancement of spatial features. Our introduced network utilized a two-stage framework, where in the first stage, a single UNet was used for building footprint localization, and in the second stage, localization features were used to guide the two-branch damage assessment network. The guidance borrowed from the first stage helped the second stage network learn more efficiently. Standard CNNs are designed to have convolutional kernels (i.e., receptive fields) of the same size. In contrast, changing the size of receptive fields, such as using atrous convolutions, has shown promising results in segmentation problems [38]. Moreover, letting the network decide which size of the receptive field to use is a superior solution [39]. Thus, we proposed a dynamic selection mechanism for our UNets, which allowed the network to adjust the receptive fields adaptively based on the weights obtained from the input information. By leveraging the proposed selective kernel module (SKM) in our UNets, the network was able to enhance the extracted features from pre- and post-disaster satellite images and focus on more important channels. We implemented our experiments on the xBD dataset [40], which is the largest dataset for building damage assessment at this time. Our experiments showed that the proposed strategy for building damage assessment yielded higher accuracies and outperformed other state-of-the-art models.