Pyramid Pooling Module-Based Semi-Siamese Network: A Benchmark Model for Assessing Building Damage from xBD Satellite Imagery Datasets

DOI Web Site 被引用文献2件 参考文献27件 オープンアクセス
  • Yanbing Bai
    Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, China
  • Junjie Hu
    Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518172, China
  • Jinhua Su
    Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, China
  • Xing Liu
    Graduate School of Information Sciences, Tohoku University, Sendai 980-8579, Japan
  • Haoyu Liu
    Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, China
  • Xianwen He
    Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, China
  • Shengwang Meng
    Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, China
  • Erick Mas
    International Research Institute of Disaster Science, Tohoku University, Sendai 980-8579, Japan
  • Shunichi Koshimura
    International Research Institute of Disaster Science, Tohoku University, Sendai 980-8579, Japan

説明

<jats:p>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 PPM-SSNet method. Finally, the consistent prediction results of our model for data from the 2011 Tohoku 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.</jats:p>

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