{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T15:17:24Z","timestamp":1778771844104,"version":"3.51.4"},"reference-count":36,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,11]],"date-time":"2020-12-11T00:00:00Z","timestamp":1607644800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific Research Fund of Renmin University of China","award":["20XNF022"],"award-info":[{"award-number":["20XNF022"]}]},{"name":"Major projects of the National Social Science Fund of China","award":["16ZDA052"],"award-info":[{"award-number":["16ZDA052"]}]},{"name":"Japan Society for the Promotion of Science (JSPS) Kakenhi Program","award":["17H06108"],"award-info":[{"award-number":["17H06108"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<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>","DOI":"10.3390\/rs12244055","type":"journal-article","created":{"date-parts":[[2020,12,13]],"date-time":"2020-12-13T23:39:36Z","timestamp":1607902776000},"page":"4055","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Pyramid Pooling Module-Based Semi-Siamese Network: A Benchmark Model for Assessing Building Damage from xBD Satellite Imagery Datasets"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5223-9425","authenticated-orcid":false,"given":"Yanbing","family":"Bai","sequence":"first","affiliation":[{"name":"Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjie","family":"Hu","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518172, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinhua","family":"Su","sequence":"additional","affiliation":[{"name":"Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xing","family":"Liu","sequence":"additional","affiliation":[{"name":"Graduate School of Information Sciences, Tohoku University, Sendai 980-8579, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0839-5460","authenticated-orcid":false,"given":"Haoyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2782-4248","authenticated-orcid":false,"given":"Xianwen","family":"He","sequence":"additional","affiliation":[{"name":"Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengwang","family":"Meng","sequence":"additional","affiliation":[{"name":"Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing 100872, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4861-5739","authenticated-orcid":false,"given":"Erick","family":"Mas","sequence":"additional","affiliation":[{"name":"International Research Institute of Disaster Science, Tohoku University, Sendai 980-8579, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8352-0639","authenticated-orcid":false,"given":"Shunichi","family":"Koshimura","sequence":"additional","affiliation":[{"name":"International Research Institute of Disaster Science, Tohoku University, Sendai 980-8579, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1038\/s41558-020-0832-y","article-title":"Multi-hazard dependencies can increase or decrease risk","volume":"10","author":"Hillier","year":"2020","journal-title":"Nat. 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