# Building Damage Assessment Based on Siamese Hierarchical Transformer Framework
###### Abstract  
The rapid and accurate damage assessment of buildings plays a critical role in disaster response. Based on pairs of pre- and post-disaster remote sensing images, effective building damage level assessment can be conducted. However, most existing methods are based on Convolutional Neural Network, which has limited ability to learn the global context. An attention mechanism helps ameliorate this problem. Hierarchical Transformer has powerful potential in the remote sensing field with strong global modeling capability. In this paper, we propose a novel two-stage damage assessment framework called SDAFomer, which embeds a symmetric hierarchical Transformer into a siamese U-Net-like network. In the first stage, the pre-disaster image is fed into a segmentation network for building localization. In the second stage, a two-branch damage classification network is established based on weights shared from the first stage. Then, pre- and post-disaster images are delivered to the network separately for damage assessment. Moreover, a spatial fusion module is designed to improve feature representation capability by building pixel-level correlation, which establishes spatial information in Swin Transformer blocks. The proposed framework achieves significant improvement on the large-scale building damage assessment dataset--xBD. remote sensing image; building damage assessment; deep learning; two-stage framework; spatial fusion +
Footnote †: journal: **MCD**  
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Footnote 1: These authors contributed equally to this work. Footnote †: journal: **MCD**  
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Footnote 1: These authors contributed equally to this work. Footnote †: journal: **MCD**  
1
Footnote 1: These authors contributed equally to this work. Footnote †: journal: **MCD**  
1
Footnote 1: These authors contributed equally to this work. Footnote †: journal: **MCD**  
1
Footnote 1: These authors contributed equally to this work. * According to whether pre-disaster or post-disaster remote sensing images are used, building damage assessment methods can be divided into the following two categories: (1) methods that use only post-disaster images; and (2) methods that use both pre-disaster and post-disaster images. For post-disaster image-based methods, damage assessment is usually conducted by image segmentation [14]. However, the outlines of buildings in post-disaster images may be blurred by the strike of disaster, and the characteristic of the buildings often changes dramatically. Collapsed buildings lose their regular geometric shapes, where the regular texture feature distribution does not apply, resulting in building assessment errors. For the above problems, if pre-disaster image of intact buildings is available and used, it can help locate the damaged parts of buildings [15]. In [16], a two-stage framework is proposed for damage detection from both pre-disaster and post-disaster images. The networks of two stages share the same weight and are responsible for building localization and damage classification, respectively. In this paper, we design our network mainly based on this idea. * According to the image processing methods used, building damage assessment methods can be divided into the following three categories: (1) manual visual interpretation, (2) traditional machine learning-based methods, and (3) deep learning-based methods. The manual visual interpretation has good specificity and relatively high accuracy, but it takes a lot of time, and the effectiveness of the interpretation depends on human experience and the time spent; thus, it may lead to missing the best time for rescue. Therefore, it is necessary to automatically perform damage assessment from remote sensing images. Many machine learning algorithms with shallow structures have been developed, such as the methods based on Support Vector Machine (SVM) [17] and Random Forest (RF) [18]. These methods have reached relatively high precision within a small number of parameters. [PERSON] et al.