{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T16:36:01Z","timestamp":1779294961909,"version":"3.51.4"},"reference-count":50,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,2]],"date-time":"2022-01-02T00:00:00Z","timestamp":1641081600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology","doi-asserted-by":"publisher","award":["2019QZKK0906, 2019QZKK0606"],"award-info":[{"award-number":["2019QZKK0906, 2019QZKK0606"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The rapid assessment of building damage in earthquake-stricken areas is of paramount importance for emergency response. The development of remote sensing technology has aided in deriving reliable and precise building damage assessments of extensive areas following disasters. It is well documented that convolutional neural network methods have superior performance in earthquake building damage assessment compared with traditional machine learning methods. However, deep learning models require a large number of samples, and sufficient numbers of samples are usually not available in the newly earthquake-stricken areas rapidly enough. At the same time, the historical samples inevitably differ from the new earthquake-affected areas due to the discrepancy of regional building characteristics. For this purpose, this study proposes a data transfer algorithm for evaluating the impact of a single historical training sample on the model performance. Then, beneficial samples are selected to transfer knowledge from the historical data for facilitating the calibration of the new model. Four models are designed with two earthquake damage building datasets and the performance of the models is compared and evaluated. The results show that the data transfer algorithm proposed in this work improves the reliability of the building damage assessment model significantly by filtering samples from the historical data that are suitable for the new task. The performance of the model built based on the data transfer method on the test set of new earthquakes task is approximately 8% higher in overall accuracy compared with the model trained directly with the new earthquake samples when the training data for the new task is only 10% of the historical data and is operating under the objective of four classes of building damage. The proposed data transfer algorithm has effectively enhanced the precision of the seismic building damage assessment in a data-limited context. Thus, it could be applicable to the building damage assessment of new disasters.<\/jats:p>","DOI":"10.3390\/rs14010201","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:06:15Z","timestamp":1641769575000},"page":"201","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Transfer Learning for Improving Seismic Building Damage Assessment"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0739-7827","authenticated-orcid":false,"given":"Qigen","family":"Lin","sequence":"first","affiliation":[{"name":"Institute for Disaster Risk Management, School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9824-5679","authenticated-orcid":false,"given":"Tianyu","family":"Ci","sequence":"additional","affiliation":[{"name":"Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University, Beijing 100875, China"},{"name":"Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0671-1124","authenticated-orcid":false,"given":"Leibin","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Resources and Environment Science, Hebei Normal University, Shijiazhuang 050024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanjit Kumar","family":"Mondal","sequence":"additional","affiliation":[{"name":"Institute for Disaster Risk Management, School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huaxiang","family":"Yin","sequence":"additional","affiliation":[{"name":"Institute for Disaster Risk Management, School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University, Beijing 100875, China"},{"name":"Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.mex.2019.01.006","article-title":"Development of seismic vulnerability index methodology for reinforced concrete buildings based on nonlinear parametric analyses","volume":"6","author":"Kassem","year":"2019","journal-title":"MethodsX"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.istruc.2019.10.016","article-title":"The efficiency of an improved seismic vulnerability index under strong ground motions","volume":"23","author":"Kassem","year":"2021","journal-title":"Structures"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1016\/j.istruc.2021.03.032","article-title":"Comparative seismic RISK assessment of existing RC buildings using seismic vulnerability index approach","volume":"32","author":"Kassem","year":"2021","journal-title":"Structures"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kassem, M.M., Nazri, F.M., Farsangi, E.N., and Ozturk, B. 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