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Building Damage Assessment Based on Siamese Hierarchical Transformer Framework

Author

Listed:
  • Yifan Da

    (College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
    These authors contributed equally to this work.)

  • Zhiyuan Ji

    (College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
    These authors contributed equally to this work.)

  • Yongsheng Zhou

    (College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China)

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.

Suggested Citation

  • Yifan Da & Zhiyuan Ji & Yongsheng Zhou, 2022. "Building Damage Assessment Based on Siamese Hierarchical Transformer Framework," Mathematics, MDPI, vol. 10(11), pages 1-23, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:11:p:1898-:d:829990
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    References listed on IDEAS

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    1. John K. Hillier & Tom Matthews & Robert L. Wilby & Conor Murphy, 2020. "Multi-hazard dependencies can increase or decrease risk," Nature Climate Change, Nature, vol. 10(7), pages 595-598, July.
    2. Gyanendra Prasad Joshi & Fayadh Alenezi & Gopalakrishnan Thirumoorthy & Ashit Kumar Dutta & Jinsang You, 2021. "Ensemble of Deep Learning-Based Multimodal Remote Sensing Image Classification Model on Unmanned Aerial Vehicle Networks," Mathematics, MDPI, vol. 9(22), pages 1-17, November.
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