Building Damage Assessment Based on Siamese Hierarchical Transformer Framework

Yifan Da, Zhiyuan Ji and Yongsheng Zhou
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Yifan Da: College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
Zhiyuan Ji: College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
Yongsheng Zhou: College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

Mathematics, 2022, vol. 10, issue 11, 1-23

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 SDAFormer, 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.

Keywords: remote sensing image; building damage assessment; deep learning; two-stage framework; spatial fusion (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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