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Although many methods have been proposed, most of them divide damaged buildings into two categories\u2014intact and damaged\u2014which is insufficient to meet practical needs. To address this issue, we present a novel convolutional neural network\u2014namely, the earthquake building damage classification net (EBDC-Net)\u2014for assessment of building damage based on post-disaster aerial images. The proposed network comprises two components: a feature extraction encoder module, and a damage classification module. The feature extraction encoder module is employed to extract semantic information on building damage and enhance the ability to distinguish between different damage levels, while the classification module improves accuracy by combining global and contextual features. The performance of EBDC-Net was evaluated using a public dataset, and a large-scale damage assessment was performed using a dataset of post-earthquake unmanned aerial vehicle (UAV) images. The results of the experiments indicate that this approach can accurately classify buildings with different damage levels. The overall classification accuracy was 94.44%, 85.53%, and 77.49% when the damage to the buildings was divided into two, three, and four categories, respectively.<\/jats:p>","DOI":"10.3390\/s22155920","type":"journal-article","created":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T04:16:55Z","timestamp":1660018615000},"page":"5920","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0045-1066","authenticated-orcid":false,"given":"Zhonghua","family":"Hong","sequence":"first","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongzheng","family":"Zhong","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiyan","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"},{"name":"National Earthquake Response Support Service, Beijing 100049, China"},{"name":"College of Civil Engineering and Architecture, Guizhou Minzu University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruyan","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0682-9157","authenticated-orcid":false,"given":"Yanling","family":"Han","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9967-7756","authenticated-orcid":false,"given":"Shuhu","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Information Technology, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changyue","family":"Zhong","sequence":"additional","affiliation":[{"name":"College of Civil Engineering and Architecture, Guizhou Minzu University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ta\u015fkin, G., Erten, E., and Alata\u015f, E.O. (2021). A Review on Multi-temporal Earthquake Damage Assessment Using Satellite Images. Change Detection and Image Time Series Analysis 2: Supervised Methods, John Wiley & Sons, Inc.","DOI":"10.1002\/9781119882299.ch5"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"022007","DOI":"10.1117\/1.JRS.13.022007","article-title":"Combined multiscale segmentation convolutional neural network for rapid damage mapping from postearthquake very high-resolution images","volume":"13","author":"Huang","year":"2019","journal-title":"J. Appl. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1007\/s10462-018-9641-3","article-title":"Recent progress in semantic image segmentation","volume":"52","author":"Liu","year":"2019","journal-title":"Artif. Intell. Rev."},{"key":"ref_4","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. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wu, C., Zhang, F., Xia, J., Xu, Y., Li, G., Xie, J., Du, Z., and Liu, R. (2021). Building damage detection using U-Net with attention mechanism from pre-and post-disaster remote sensing datasets. Remote Sens., 13.","DOI":"10.3390\/rs13050905"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xiao, H., Peng, Y., Tan, H., and Li, P. (2021, January 5\u20139). Dynamic Cross Fusion Network for Building-Based Damage Assessment. Proceedings of the 2021 IEEE International Conference on Multimedia and Expo (ICME), Shenzhen, China.","DOI":"10.1109\/ICME51207.2021.9428414"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.isprsjprs.2021.02.016","article-title":"Learning from multimodal and multitemporal earth observation data for building damage mapping","volume":"175","author":"Adriano","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"1040","DOI":"10.1080\/01431161.2019.1655175","article-title":"Integration of super-pixel segmentation and deep-learning methods for evaluating earthquake-damaged buildings using single-phase remote sensing imagery","volume":"41","author":"Song","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yang, W., Zhang, X., and Luo, P. (2021). Transferability of convolutional neural network models for identifying damaged buildings due to earthquake. Remote Sens., 13.","DOI":"10.3390\/rs13030504"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Duarte, D., Nex, F., Kerle, N., and Vosselman, G. (2018). Multi-resolution feature fusion for image classification of building damages with convolutional neural networks. Remote Sens., 10.","DOI":"10.3390\/rs10101636"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ji, M., Liu, L., Zhang, R.F., and Buchroithner, M. (2020). Discrimination of earthquake-induced building destruction from space using a pretrained CNN model. Appl. Sci., 10.","DOI":"10.3390\/app10020602"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Nex, F., Duarte, D., Tonolo, F.G., and Kerle, N. (2019). Structural building damage detection with deep learning: Assessment of a state-of-the-art CNN in operational conditions. Remote Sens., 11.","DOI":"10.3390\/rs11232765"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ishraq, A., Lima, A.A., Kabir, M.M., Rahman, M.S., and Mridha, M. (2022, January 23\u201325). Assessment of Building Damage on Post-Hurricane Satellite Imagery using improved CNN. Proceedings of the 2022 International Conference on Decision Aid Sciences and Applications (DASA), Chiangrai, Thailand.","DOI":"10.1109\/DASA54658.2022.9765025"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1080\/19475705.2015.1020887","article-title":"Integrated detection and analysis of earthquake disaster information using airborne data","volume":"7","author":"Cao","year":"2016","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ci, T., Liu, Z., and Wang, Y. (2019). Assessment of the degree of building damage caused by disaster using convolutional neural networks in combination with ordinal regression. Remote Sens., 11.","DOI":"10.3390\/rs11232858"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ma, H., Liu, Y., Ren, Y., Wang, D., Yu, L., and Yu, J. (2020). Improved CNN classification method for groups of buildings damaged by earthquake, based on high resolution remote sensing images. Remote Sens., 12.","DOI":"10.3390\/rs12020260"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Matin, S.S., and Pradhan, B. (2021). Challenges and limitations of earthquake-induced building damage mapping techniques using remote sensing images-A systematic review. Geocarto Int., 1\u201327.","DOI":"10.1080\/10106049.2021.1933213"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4287","DOI":"10.1109\/TGRS.2020.3014312","article-title":"Scene-driven multitask parallel attention network for building extraction in high-resolution remote sensing images","volume":"59","author":"Guo","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","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 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 15\u201320). Dual attention network for scene segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.neucom.2018.02.105","article-title":"Hyperspectral image classification using spectral-spatial LSTMs","volume":"328","author":"Zhou","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yin, J., Qi, C., Chen, Q., and Qu, J. (2021). Spatial-spectral network for hyperspectral image classification: A 3-D CNN and Bi-LSTM framework. Remote Sens., 13.","DOI":"10.3390\/rs13122353"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, Q., Zhou, F., Hang, R., and Yuan, X. (2017). Bidirectional-convolutional LSTM based spectral-spatial feature learning for hyperspectral image classification. Remote Sens., 9.","DOI":"10.3390\/rs9121330"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_29","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv."},{"key":"ref_30","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5920\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:05:40Z","timestamp":1760141140000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5920"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,8]]},"references-count":30,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["s22155920"],"URL":"https:\/\/doi.org\/10.3390\/s22155920","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,8]]}}}