该研究主要使用来自全世界范围的85万个受灾（地震/海啸、台风、洪水、火山和山火）建筑物损毁相关的大规模卫星影像数据集，构建了基于监督学习语义分割算法的建筑物损毁分类基准模型。该研究引入空洞卷积，设计了一种基于金字塔池化模块的Semi-Siamese网络，该网络可以同时开展建筑物提取和建筑物损毁分类多任务学习，实现了端对端自动建筑物损毁提取。此外，我们基于该基准模型，开展了针对2011年东日本大地震的建筑物损毁评估模型泛化实证研究。

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.

引用：Bai, Y., Hu, J., Su, J., Liu, X., Liu, H., He, X., Meng, S., Mas, E. & Koshimura, S., Pyramid Pooling Module-Based Semi-Siamese Network: A Benchmark Model for Assessing Building Damage from xBD Satellite Imagery Datasets, Remote Sensing. 2020（24）12：p. 1-20 20 p., 4055.

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