{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T13:50:55Z","timestamp":1778593855124,"version":"3.51.4"},"reference-count":51,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T00:00:00Z","timestamp":1659312000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"State Key Research and Development Plan","award":["2018YFB100046"],"award-info":[{"award-number":["2018YFB100046"]}]},{"name":"State Key Research and Development Plan","award":["DD20191016"],"award-info":[{"award-number":["DD20191016"]}]},{"name":"China Geological Survey Project","award":["2018YFB100046"],"award-info":[{"award-number":["2018YFB100046"]}]},{"name":"China Geological Survey Project","award":["DD20191016"],"award-info":[{"award-number":["DD20191016"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The rapid and accurate discovery of damage information of the affected buildings is of great significance for postdisaster emergency rescue. In some related studies, the models involved can detect damaged buildings relatively accurately, but their time cost is high. Models that can guarantee both detection accuracy and high efficiency are urgently needed. In this paper, we propose a new transfer-learning deep attention network (TDA-Net). It can achieve a balance of accuracy and efficiency. The benchmarking network for TDA-Net uses a pair of deep residual networks and is pretrained on a large-scale dataset of disaster-damaged buildings. The pretrained deep residual networks have strong sensing properties on the damage information, which ensures the effectiveness of the network in prefeature grasping. In order to make the network have a more robust perception of changing features, a set of deep attention bidirectional encoding and decoding modules is connected after the TDA-Net benchmark network. When performing a new task, only a small number of samples are needed to train the network, and the damage information of buildings in the whole area can be extracted. The bidirectional encoding and decoding structure of the network allows two images to be input into the model independently, which can effectively capture the features of a single image, thereby improving the detection accuracy. Our experiments on the xView2 dataset and three datasets of disaster regions achieve high detection accuracy, which demonstrates the feasibility of our method.<\/jats:p>","DOI":"10.3390\/rs14153687","type":"journal-article","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T21:01:24Z","timestamp":1659387684000},"page":"3687","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["TDA-Net: A Novel Transfer Deep Attention Network for Rapid Response to Building Damage Discovery"],"prefix":"10.3390","volume":"14","author":[{"given":"Haiming","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2806-858X","authenticated-orcid":false,"given":"Mingchang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongxian","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guorui","family":"Ma","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112636","DOI":"10.1016\/j.rse.2021.112636","article-title":"Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man-made disasters","volume":"265","author":"Zheng","year":"2021","journal-title":"Remote Sens. 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