Pyramid pooling module-based semi-siamese network: A benchmark model for assessing building damage from xbd satellite imagery datasets

Yanbing Bai, Junjie Hu, Jinhua Su, Xing Liu, Haoyu Liu, Xianwen He, Shengwang Meng, Erick Mas, Shunichi Koshimura

研究成果: Article査読

7 被引用数 (Scopus)

抄録

Most mainstream research on assessing building damage using satellite imagery is based on scattered datasets and lacks unified standards and methods to quantify and compare the performance of different models. To mitigate these problems, the present study develops a novel end-to-end benchmark model, termed the pyramid pooling module semi-Siamese network (PPM-SSNet), based on a large-scale xBD satellite imagery dataset. The high precision of the proposed model is achieved by adding residual blocks with dilated convolution and squeeze-and-excitation blocks into the network. Simultaneously, the highly automated process of satellite imagery input and damage classification result output is reached by employing concurrent learned attention mechanisms through a semi-Siamese network for end-to-end input and output purposes. Our proposed method achieves F1 scores of 0.90, 0.41, 0.65, and 0.70 for the undamaged, minor-damaged, major-damaged, and destroyed building classes, respectively. From the perspective of end-to-end methods, the ablation experiments and comparative analysis confirm the effectiveness and originality of the proposed method. Finally, the consistent prediction results of our model for data from the 2011 Great East Japan Earthquake verify the high performance of our model in terms of the domain shift problem, which implies that it is effective for evaluating future disasters.

本文言語English
論文番号4055
ページ(範囲)1-20
ページ数20
ジャーナルRemote Sensing
12
24
DOI
出版ステータスPublished - 2020 12 2

ASJC Scopus subject areas

  • 地球惑星科学(全般)

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