{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T20:59:03Z","timestamp":1783112343443,"version":"3.54.6"},"reference-count":34,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,20]],"date-time":"2019-12-20T00:00:00Z","timestamp":1576800000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["2017YFC1500902"],"award-info":[{"award-number":["2017YFC1500902"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>An important and effective method for the preliminary mitigation and relief of an earthquake is the rapid estimation of building damage via high spatial resolution remote sensing technology. Traditional object detection methods only use artificially designed shallow features on post-earthquake remote sensing images, which are uncertain and complex background environment and time-consuming feature selection. The satisfactory results from them are often difficult. Therefore, this study aims to apply the object detection method You Only Look Once (YOLOv3) based on the convolutional neural network (CNN) to locate collapsed buildings from post-earthquake remote sensing images. Moreover, YOLOv3 was improved to obtain more effective detection results. First, we replaced the Darknet53 CNN in YOLOv3 with the lightweight CNN ShuffleNet v2. Second, the prediction box center point, XY loss, and prediction box width and height, WH loss, in the loss function was replaced with the generalized intersection over union (GIoU) loss. Experiments performed using the improved YOLOv3 model, with high spatial resolution aerial remote sensing images at resolutions of 0.5 m after the Yushu and Wenchuan earthquakes, show a significant reduction in the number of parameters, detection speed of up to 29.23 f\/s, and target precision of 90.89%. Compared with the general YOLOv3, the detection speed improved by 5.21 f\/s and its precision improved by 5.24%. Moreover, the improved model had stronger noise immunity capabilities, which indicates a significant improvement in the model\u2019s generalization. Therefore, this improved YOLOv3 model is effective for the detection of collapsed buildings in post-earthquake high-resolution remote sensing images.<\/jats:p>","DOI":"10.3390\/rs12010044","type":"journal-article","created":{"date-parts":[[2019,12,23]],"date-time":"2019-12-23T03:15:01Z","timestamp":1577070901000},"page":"44","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":134,"title":["Detection of Collapsed Buildings in Post-Earthquake Remote Sensing Images Based on the Improved YOLOv3"],"prefix":"10.3390","volume":"12","author":[{"given":"Haojie","family":"Ma","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yalan","family":"Liu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhuan","family":"Ren","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingxian","family":"Yu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,20]]},"reference":[{"key":"ref_1","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_2","unstructured":"Chen, W. (2007). Research of Remote Sensing Application Technology Based on Earthquake Disaster Assessment, China Earthquake Administration Lanzhou Institute of Seismology."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Cooner, A., Shao, Y., and Campbell, J. (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_4","doi-asserted-by":"crossref","first-page":"1521","DOI":"10.1193\/060211EQS126M","article-title":"Damage detection using high-resolution SAR imagery in the 2009 L\u2019Aquila, Italy, Earthquake","volume":"29","author":"Uprety","year":"2013","journal-title":"Earthq. Spectra"},{"key":"ref_5","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_6","doi-asserted-by":"crossref","unstructured":"Gong, L., Li, Q., and Zhang, J. (2013, January 21\u201326). Earthquake building damage detection with object-oriented change detection. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium-IGARSS, Melbourne, Australia.","DOI":"10.1109\/IGARSS.2013.6723627"},{"key":"ref_7","first-page":"477","article-title":"Damaged building detection based on GF-1 satellite remote sensing image: A case study for Nepal MS8.1 earthquake","volume":"38","author":"Ye","year":"2016","journal-title":"Acta Seismol. Sin."},{"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","unstructured":"Janalipour, M., and Mohammadzadeh, A. (2017). A fuzzy-ga based decision making system for detecting damaged buildings from high-spatial resolution optical images. Remote Sens., 9.","DOI":"10.3390\/rs9040349"},{"key":"ref_10","first-page":"84","article-title":"Real-time airplane detection algorithm in remote-sensing images based on improved YOLOv3","volume":"45","author":"Dai","year":"2018","journal-title":"Opto Electron. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., and Darrell, T. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_12","unstructured":"Ren, S., He, K., and Girshick, R. (2015, January 7\u201312). Faster R-CNN: Towards real-time object detection with region proposal networks. Proceedings of the Annual Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., and Doll\u00e1r, P. (2017, January 21\u201326). Mask R-CNN. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., and Girshick, R. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., and Erhan, D. (2016, January 8\u201316). SSD: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_17","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Han, X., Zhong, Y., and Zhang, L. (2017). An efficient and robust integrated geospatial object detection framework for high spatial resolution remote sensing imagery. Remote Sens., 9.","DOI":"10.3390\/rs9070666"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7405","DOI":"10.1109\/TGRS.2016.2601622","article-title":"Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images","volume":"54","author":"Cheng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","first-page":"32","article-title":"Application of improved YOLOv3 in aircraft recognition of remote sensing images","volume":"26","author":"Zheng","year":"2019","journal-title":"Electron. Opt. Control."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"89","DOI":"10.5194\/isprs-annals-IV-2-89-2018","article-title":"Satellite image classification of building damages using airborne and satellite image samples in a deep learning approach","volume":"4","author":"Duarte","year":"2018","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The PASCAL visual object classes (VOC) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_24","unstructured":"Perez, L., and Wang, J. (2017). The effectiveness of data augmentation in image classification using deep learning. arXiv."},{"key":"ref_25","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., and Ren, S. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., and Zheng, H. (2018, January 8\u201314). ShuffleNet v2: Practical guidelines for efficient CNN architecture design. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_8"},{"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","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., and Lin, M. (2018, January 18\u201323). ShuffleNet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., and Gwak, J.Y. (2019, January 16\u201319). Generalized intersection over union: A metric and a loss for bounding box regression. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Los Angeles, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.compag.2019.01.012","article-title":"Apple detection during different growth stages in orchards using the improved YOLO-V3 model","volume":"157","author":"Tian","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Benjdira, B., Khursheed, T., and Koubaa, A. (2019, January 5\u20137). Car detection using unmanned aerial vehicles: Comparison between Faster R-CNN and YOLOv3. Proceedings of the International Conference on Unmanned Vehicle Systems-Oman (UVS), Sultan Qaboos Univ, Muscat, Oman.","DOI":"10.1109\/UVS.2019.8658300"},{"key":"ref_33","unstructured":"Zhao, Y., Ren, H., and Cao, D. (2018, January 22\u201327). The research of building earthquake damage object-oriented change detection based on ensemble classifier with remote sensing image. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium-IGARSS, Valencia, Spain."},{"key":"ref_34","first-page":"13","article-title":"Object-oriented collapsed building extraction from multi-source remote sensing imagery based on SVM","volume":"33","author":"Wen","year":"2015","journal-title":"North China Earthq. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/1\/44\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:44:15Z","timestamp":1760190255000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/1\/44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,20]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["rs12010044"],"URL":"https:\/\/doi.org\/10.3390\/rs12010044","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,20]]}}}