{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T16:23:14Z","timestamp":1774369394369,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,2,28]],"date-time":"2021-02-28T00:00:00Z","timestamp":1614470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2019YFE0127400"],"award-info":[{"award-number":["2019YFE0127400"]}]},{"name":"KAKENHI","award":["19K20309"],"award-info":[{"award-number":["19K20309"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41671391"],"award-info":[{"award-number":["41671391"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41922043"],"award-info":[{"award-number":["41922043"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871287"],"award-info":[{"award-number":["41871287"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The building damage status is vital to plan rescue and reconstruction after a disaster and is also hard to detect and judge its level. Most existing studies focus on binary classification, and the attention of the model is distracted. In this study, we proposed a Siamese neural network that can localize and classify damaged buildings at one time. The main parts of this network are a variety of attention U-Nets using different backbones. The attention mechanism enables the network to pay more attention to the effective features and channels, so as to reduce the impact of useless features. We train them using the xBD dataset, which is a large-scale dataset for the advancement of building damage assessment, and compare their result balanced F (F1) scores. The score demonstrates that the performance of SEresNeXt with an attention mechanism gives the best performance among single models, with the F1 score reaching 0.787. To improve the accuracy, we fused the results and got the best overall F1 score of 0.792. To verify the transferability and robustness of the model, we selected the dataset on the Maxar Open Data Program of two recent disasters to investigate the performance. By visual comparison, the results show that our model is robust and transferable.<\/jats:p>","DOI":"10.3390\/rs13050905","type":"journal-article","created":{"date-parts":[[2021,2,28]],"date-time":"2021-02-28T01:51:43Z","timestamp":1614477103000},"page":"905","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":107,"title":["Building Damage Detection Using U-Net with Attention Mechanism from Pre- and Post-Disaster Remote Sensing Datasets"],"prefix":"10.3390","volume":"13","author":[{"given":"Chuyi","family":"Wu","sequence":"first","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1475-8480","authenticated-orcid":false,"given":"Feng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China"},{"name":"Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou 310028, China"}]},{"given":"Junshi","family":"Xia","sequence":"additional","affiliation":[{"name":"Geoinformatics Unit, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan"}]},{"given":"Yichen","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China"}]},{"given":"Guoqing","family":"Li","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Jibo","family":"Xie","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Zhenhong","family":"Du","sequence":"additional","affiliation":[{"name":"Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou 310028, China"}]},{"given":"Renyi","family":"Liu","sequence":"additional","affiliation":[{"name":"Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou 310028, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1038\/s41893-020-0536-3","article-title":"Effects of a natural disaster on mortality risks over the longer term","volume":"3","author":"Frankenberg","year":"2020","journal-title":"Nat. Sustain."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.proeps.2015.08.063","article-title":"Automatic Detection of Damaged Buildings after Earthquake Hazard by Using Remote Sensing and Information Technologies","volume":"15","author":"Menderes","year":"2015","journal-title":"Procedia Earth Planet. Sci."},{"key":"ref_3","unstructured":"Xu, J.Z., Lu, W., Li, Z., Khaitan, P., and Zaytseva, V. (2019). Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cooner, A.J., Shao, Y., and Campbell, J.B. (2016). Detection of urban damage using remote sensing and machine learning algorithms: Revisiting the 2010 Haiti earthquake. Remote Sens., 8.","DOI":"10.3390\/rs8100868"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2876","DOI":"10.1109\/JPROC.2012.2196404","article-title":"Remote sensing and earthquake damage assessment: Experiences, limits, and perspectives","volume":"100","author":"Gamba","year":"2012","journal-title":"Proc. IEEE"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1109\/LGRS.2011.2170657","article-title":"Postearthquake building damage assessment using multi-mutual information from pre-event optical image and postevent SAR image","volume":"9","author":"Wang","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3411","DOI":"10.1080\/01431161003727697","article-title":"Context-based mapping of damaged buildings from high-resolution optical satellite images","volume":"31","author":"Vu","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Adriano, B., Xia, J., Baier, G., Yokoya, N., and Koshimura, S. (2019). Multi-source data fusion based on ensemble learning for rapid building damage mapping during the 2018 Sulawesi earthquake and Tsunami in Palu, Indonesia. Remote Sens., 11.","DOI":"10.3390\/rs11070886"},{"key":"ref_9","unstructured":"Weber, E., and Kan\u00e9, H. (2020). Building Disaster Damage Assessment in Satellite Imagery with Multi-Temporal Fusion. arXiv."},{"key":"ref_10","unstructured":"Chen, S.A., Escay, A., Haberland, C., Schneider, T., Staneva, V., and Choe, Y. (2018). Benchmark Dataset for Automatic Damaged Building Detection from Post-Hurricane Remotely Sensed Imagery. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.isprsjprs.2013.06.011","article-title":"A comprehensive review of earthquake-induced building damage detection with remote sensing techniques","volume":"84","author":"Dong","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Duarte, D., Nex, F., Kerle, N., and Vosselman, G. (2018, January 4\u20137). Satellite image classification of building damages using airborne and satellite image samples in a deep learning approach. Proceedings of the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Copernicus GmbH, Riva del Garda, Italy.","DOI":"10.5194\/isprs-annals-IV-2-89-2018"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3499","DOI":"10.1007\/s11434-010-4078-3","article-title":"Yushu earthquake synergic analysis using multimodal SAR datasets","volume":"55","author":"Guo","year":"2010","journal-title":"Chin. Sci. Bull."},{"key":"ref_14","first-page":"17","article-title":"Urban Change Detection Based on High Resolution SAR and Optical Remote Sensing Data","volume":"5","author":"Mao","year":"2019","journal-title":"Urban Geotech. Investig. Surv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ji, M., Liu, L., and Buchroithner, M. (2018). Identifying collapsed buildings using post-earthquake satellite imagery and convolutional neural networks: A case study of the 2010 Haiti Earthquake. Remote Sens., 10.","DOI":"10.3390\/rs10111689"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3013","DOI":"10.1080\/01431160601094492","article-title":"Rapid damage assessment of built-up structures using VHR satellite data in tsunami-affected areas","volume":"28","author":"Pesaresi","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gamba, P., Dell\u2019Acqua, F., and Odasso, L. (2007, January 11\u201313). Object-oriented building damage analysis in VHR optical satellite images of the 2004 Tsunami over Kalutara, Sri Lanka. Proceedings of the 2007 Urban Remote Sensing Joint Event, Paris, France.","DOI":"10.1109\/URS.2007.371787"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.isprsjprs.2011.12.004","article-title":"Building-damage detection using pre- and post-seismic high-resolution satellite stereo imagery: A case study of the May 2008 Wenchuan earthquake","volume":"68","author":"Tong","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"156349","DOI":"10.1109\/ACCESS.2019.2947286","article-title":"Remote sensing image change detection based on information transmission and attention mechanism","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"126385","DOI":"10.1109\/ACCESS.2020.3008036","article-title":"Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis","volume":"8","author":"Khelifi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1845","DOI":"10.1109\/LGRS.2017.2738149","article-title":"Change Detection Based on Deep Siamese Convolutional Network for Optical Aerial Images","volume":"14","author":"Zhan","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ghaffarian, S., Kerle, N., Pasolli, E., and Arsanjani, J.J. (2019). Post-disaster building database updating using automated deep learning: An integration of pre-disaster OpenStreetMap and multi-temporal satellite data. Remote Sens., 11.","DOI":"10.3390\/rs11202427"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sun, Y., Zhang, X., Huang, J., Wang, H., and Xin, Q. (2020). Fine-Grained Building Change Detection From Very High-Spatial-Resolution Remote Sensing Images Based on Deep Multitask Learning. IEEE Geosci. Remote Sens. Lett., 1\u20135.","DOI":"10.1109\/LGRS.2020.3018858"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7209","DOI":"10.1109\/TGRS.2019.2912301","article-title":"Road detection and centerline extraction via deep recurrent convolutional neural network U-Net","volume":"57","author":"Yang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2307\/143141","article-title":"A Computer Movie Simulating Urban Growth in the Detroit Region","volume":"46","author":"Tobler","year":"1970","journal-title":"Econ. Geogr."},{"key":"ref_26","first-page":"2204","article-title":"Recurrent models of visual attention","volume":"3","author":"Mnih","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hao, H., Baireddy, S., Bartusiak, E.R., Konz, L., LaTourette, K., Gribbons, M., Chan, M., Comer, M.L., and Delp, E.J. (2020). An attention-based system for damage assessment using satellite imagery. arXiv.","DOI":"10.1109\/IGARSS47720.2021.9554054"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1109\/TGRS.2019.2933609","article-title":"Learning to Pay Attention on Spectral Domain: A Spectral Attention Module-Based Convolutional Network for Hyperspectral Image Classification","volume":"58","author":"Mou","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","unstructured":"Gupta, R., Hosfelt, R., Sajeev, S., Patel, N., Goodman, B., Doshi, J., Heim, E., Choset, H., and Gaston, M. (2019). xBD: A Dataset for Assessing Building Damage from Satellite Imagery. arXiv."},{"key":"ref_30","unstructured":"(2020, November 24). Open Data Program. Available online: https:\/\/www.maxar.com\/open-data."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Castello, R., Roquette, S., Esguerra, M., Guerra, A., and Scartezzini, J.L. (2019). Deep learning in the built environment: Automatic detection of rooftop solar panels using Convolutional Neural Networks. J. Phys. Conf. Ser.","DOI":"10.1088\/1742-6596\/1343\/1\/012034"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ebrahim, M., Al-Ayyoub, M., and Alsmirat, M.A. (2019, January 11\u201319). Will Transfer Learning Enhance ImageNet Classification Accuracy Using ImageNet-Pretrained Models?. Proceedings of the 2019 10th International Conference on Information and Communication Systems (ICICS), Irbid, Jordan.","DOI":"10.1109\/IACS.2019.8809114"},{"key":"ref_34","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., and Kainz, B. (2018). Attention U-Net: Learning Where to Look for the Pancreas. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1007\/s13748-016-0094-0","article-title":"Learning from imbalanced data: Open challenges and future directions","volume":"5","author":"Krawczyk","year":"2016","journal-title":"Prog. Artif. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Kai, L., and Li, F.-F. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_38","unstructured":"Telgarsky, M. (2016, January 23\u201326). Benefits of depth in neural networks. Proceedings of the Journal of Machine Learning Research: Workshop and Conference Proceedings, New York, NY, USA."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-Excitation Networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated residual transformations for deep neural networks. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_42","unstructured":"Chen, Y., Li, J., Xiao, H., Jin, X., Yan, S., and Feng, J. (2017, January 3\u20139). Dual path networks. Proceedings of the NIPS\u201917: Proceedings of the 31st International Conference on Neural Information Processing, Long beach, CA, USA."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/S0034-4257(97)00083-7","article-title":"Selecting and interpreting measures of thematic classification accuracy","volume":"62","author":"Stehman","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_44","first-page":"237","article-title":"Information retrieval; 2nd ed.; Butterworth, 1978","volume":"11","year":"1979","journal-title":"J. librariansh."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1023\/A:1009982220290","article-title":"An evaluation of statistical approaches to text categorization","volume":"1","author":"Yang","year":"1999","journal-title":"Inf. Retr. Boston"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Garcia-Garcia, A., Orts-Escolano, S., Oprea, S.O., Villena-Martinez, V., and Garcia-Rodriguez, J. (2017). A review on deep learning techniques applied to semantic segmentation. arXiv.","DOI":"10.1016\/j.asoc.2018.05.018"},{"key":"ref_47","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 7\u20139). Adam: A method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings; International Conference on Learning Representations, ICLR, San Diego, CA, USA."},{"key":"ref_48","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"997","DOI":"10.14358\/PERS.77.10.0997","article-title":"A comprehensive analysis of building damage in the 12 January 2010 MW7 Haiti earthquake using high-resolution satelliteand aerial imagery","volume":"77","author":"Corbane","year":"2011","journal-title":"Photogramm. Eng. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/5\/905\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:30:16Z","timestamp":1760160616000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/5\/905"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,28]]},"references-count":49,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["rs13050905"],"URL":"https:\/\/doi.org\/10.3390\/rs13050905","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,28]]}}}