{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T02:44:40Z","timestamp":1775616280747,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2020,8,13]],"date-time":"2020-08-13T00:00:00Z","timestamp":1597276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2017YFB0504100"],"award-info":[{"award-number":["2017YFB0504100"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41901386"],"award-info":[{"award-number":["41901386"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Natural Science Foundation of Chongqing","award":["cstc2019jcyj-msxmX0548"],"award-info":[{"award-number":["cstc2019jcyj-msxmX0548"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Extracting damage information of buildings after an earthquake is crucial for emergency rescue and loss assessment. Low-altitude remote sensing by unmanned aerial vehicles (UAVs) for emergency rescue has unique advantages. In this study, we establish a remote sensing information-extraction method that combines ultramicro oblique UAV and infrared thermal imaging technology to automatically detect the structural damage of buildings and cracks in external walls. The method consists of four parts: (1) 3D live-action modeling and building structure analysis based on ultramicro oblique images; (2) extraction of damage information of buildings; (3) detection of cracks in walls based on infrared thermal imaging; and (4) integration of detection systems for information of earthquake-damaged buildings. First, a 3D live-action building model is constructed. A multi-view structure image for segmentation can be obtained based on this method. Second, a method of extracting information on damage to building structures using a 3D live-action building model as the geographic reference is proposed. Damage information of the internal structure of the building can be obtained based on this method. Third, based on analyzing the temperature field distribution on the exterior walls of earthquake-damaged buildings, an automatic method of detecting cracks in the walls by using infrared thermal imaging is proposed. Finally, the damage information detection and assessment system is researched and developed, and the system is integrated. Taking earthquake search-and-rescue simulation as an example, the effectiveness of this method is verified. The damage distribution in the internal structure and external walls of buildings in this area is obtained with an accuracy of 78%.<\/jats:p>","DOI":"10.3390\/rs12162621","type":"journal-article","created":{"date-parts":[[2020,8,14]],"date-time":"2020-08-14T08:28:35Z","timestamp":1597393715000},"page":"2621","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":86,"title":["Automatic Detection of Earthquake-Damaged Buildings by Integrating UAV Oblique Photography and Infrared Thermal Imaging"],"prefix":"10.3390","volume":"12","author":[{"given":"Rui","family":"Zhang","sequence":"first","affiliation":[{"name":"Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3187-9041","authenticated-orcid":false,"given":"Heng","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Building and Real Estate, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0410-219X","authenticated-orcid":false,"given":"Kaifeng","family":"Duan","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Tongji University, Shanghai 200092, China"}]},{"given":"Shucheng","family":"You","sequence":"additional","affiliation":[{"name":"Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China"}]},{"given":"Ke","family":"Liu","sequence":"additional","affiliation":[{"name":"Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China"}]},{"given":"Futao","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Yong","family":"Hu","sequence":"additional","affiliation":[{"name":"Chongqing Institute of Surveying and Monitoring for Planning and Natural Resources, Chongqing 400120 China"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,13]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.pce.2006.02.024","article-title":"Remote sensing and earthquakes: A review","volume":"31","author":"Tronin","year":"2006","journal-title":"Phys. Chem. Earth"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1142\/S1793431107000122","article-title":"Remote sensing technologies in post-disaster damage assessment","volume":"1","author":"Yamazaki","year":"2007","journal-title":"J. Earthq. Tsunami"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3511","DOI":"10.1109\/TGRS.2010.2047260","article-title":"Using aerial imagery and gis in automated building footprint extraction and shape recognition for earthquake risk assessment of urban inventories","volume":"48","author":"Sahar","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"S207","DOI":"10.1193\/1.2107967","article-title":"Use of remote sensing technologies for building damage assessment after the 2003 Bam, Iran, earthquake-preface to remote sensing papers","volume":"21","author":"Eguchi","year":"2005","journal-title":"Earthq. Spectra"},{"key":"ref_6","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_7","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_8","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.engstruct.2013.12.033","article-title":"Earthquake early warning application to buildings","volume":"60","author":"Cheng","year":"2014","journal-title":"Eng. Struct."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Nex, F., Duarte, D., Steenbeek, A., and Kerle, N. (2019). Towards real-time building damage mapping with low-cost UAV solutions. Remote Sens., 11.","DOI":"10.3390\/rs11030287"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.1109\/LGRS.2015.2429894","article-title":"Unsupervised detection of earthquake-triggered roof-holes from UAV images using joint color and shape features","volume":"12","author":"Li","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.isprsjprs.2015.03.016","article-title":"Identification of damage in buildings based on gaps in 3D point clouds from very high resolution oblique airborne images","volume":"105","author":"Vetrivel","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cichoci\u0144ski, P., Jurczyszyn, D., and Kochan, M. (2014, January 22\u201324). Proposal of data source and method for creating 3D models of buildings. Proceedings of the 9th International Conference \u201cEnvironmental Engineering 2014\u201d, Vilnius, Lithuania.","DOI":"10.3846\/enviro.2014.199"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4761","DOI":"10.1109\/ACCESS.2019.2962909","article-title":"An optimal monitoring model of desertification in naiman banner based on feature space utilizing Landsat8 Oli image","volume":"8","author":"Guo","year":"2020","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1080\/2150704X.2019.1692389","article-title":"Detecting damaged buildings using a texture feature contribution index from post-earthquake remote sensing images","volume":"11","author":"Wei","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.epsl.2009.06.004","article-title":"Thermal imaging of alluvial fans: A new technique for remote classification of sedimentary features","volume":"285","author":"Hardgrove","year":"2009","journal-title":"Earth Planet. Sci. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/S0951-8320(01)00105-3","article-title":"Earthquake risk assessment of building structures","volume":"74","author":"Ellingwood","year":"2001","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1080\/19475705.2016.1265013","article-title":"A GIS-based approach for earthquake loss estimation based on the immediate extraction of damaged buildings","volume":"8","author":"Ranjbar","year":"2017","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"097292","DOI":"10.1117\/1.JRS.9.097292","article-title":"Rapid three-dimensional detection approach for building damage due to earthquakes by the use of parallel processing of unmanned aerial vehicle imagery","volume":"9","author":"Hong","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"S415","DOI":"10.1193\/112916eqs210m","article-title":"Improving post-earthquake building safety evaluation using the 2015 Gorkha, Nepal, Earthquake rapid visual damage assessment data","volume":"33","author":"Didier","year":"2017","journal-title":"Earthq. Spectra"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.autcon.2017.06.024","article-title":"Remote sensing of concrete bridge decks using unmanned aerial vehicle infrared thermography","volume":"83","author":"Omar","year":"2017","journal-title":"Autom. Constr."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"102994","DOI":"10.1016\/j.autcon.2019.102994","article-title":"Automated regional seismic damage assessment of buildings using an unmanned aerial vehicle and a convolutional neural network","volume":"109","author":"Xiong","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1981","DOI":"10.1007\/s11069-016-2530-7","article-title":"Building damage analysis for the updated building dataset of Istanbul","volume":"84","author":"Konukcu","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"016203","DOI":"10.1117\/1.OE.51.1.016203","article-title":"Generalized multiple kernel framework for multiclass geospatial objects detection in high-resolution remote sensing images","volume":"51","author":"Li","year":"2012","journal-title":"Opt. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1061\/(ASCE)SU.1943-5428.0000298","article-title":"Improving data acquisition efficiency: Systematic accuracy evaluation of GNSS-assisted aerial triangulation in UAS operations","volume":"146","author":"Benjamin","year":"2020","journal-title":"J. Surv. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.isprsjprs.2019.11.016","article-title":"A UAV-based panoramic oblique photogrammetry (POP) approach using spherical projection","volume":"159","author":"Zhang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.cageo.2015.07.008","article-title":"Computers & geosciences from oblique photogrammetry to a 3D model\u2014Structural modeling of Kilen, eastern North Greenland","volume":"83","author":"Svennevig","year":"2015","journal-title":"Comput. Geosci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, S., Yang, H.Z., Wang, S.S., and Hu, Q.W. (2018). Surveying and digital restoration of towering architectural heritage in harsh environments: A case study of the millennium ancient watchtower in Tibet. Sustainability, 10.","DOI":"10.3390\/su10093138"},{"key":"ref_28","first-page":"107","article-title":"Evaluation of the infrared thermography technique for capillarity moisture detection in buildings","volume":"11","author":"Rocha","year":"2018","journal-title":"Proc. Struct. Integr."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"65423E","DOI":"10.1117\/12.718055","article-title":"Integrated homeland security system with passive thermal imaging and advanced video analytics","volume":"6542","author":"Francisco","year":"2007","journal-title":"Infrared Technol. Appl. XXXIII"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1080\/19475705.2020.1721573","article-title":"Detection model of soil salinization information in the Yellow River Delta based on feature space models with typical surface parameters derived from Landsat8 OLI image","volume":"11","author":"Guo","year":"2020","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s11069-012-0482-0","article-title":"Extracting building stock information from optical satellite imagery for mapping earthquake exposure and its vulnerability","volume":"68","author":"Ehrlich","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1007\/s11069-010-9624-4","article-title":"Earthquake characteristics and building damage in high-intensity areas of Wenchuan earthquake I: Yingxiu town","volume":"57","author":"He","year":"2011","journal-title":"Nat. Hazards"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.autcon.2016.05.002","article-title":"Automated progress monitoring system for linear infrastructure projects using satellite remote sensing","volume":"68","author":"Behnam","year":"2016","journal-title":"Autom. Constr."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.apenergy.2013.03.066","article-title":"Effects of individual climatic parameters on the infrared thermography of buildings","volume":"110","author":"Lehmann","year":"2013","journal-title":"Appl. Energy"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.jobe.2018.07.018","article-title":"Detection of hidden corrosion in metal roofing shingles utilizing infrared thermography","volume":"20","author":"Wicker","year":"2018","journal-title":"J. Build. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Moropoulou, A., Avdelidis, N.P., Karoglou, M., and Delegou, E.T. (2018). Multispectral applications of infrared thermography in the diagnosis and protection of built cultural heritage. Appl. Sci., 8.","DOI":"10.3390\/app8020284"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1080\/2150704X.2015.1121299","article-title":"The accuracy of aerial triangulation products automatically generated from hyper-spatial resolution digital aerial photography","volume":"7","author":"Zhang","year":"2016","journal-title":"Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2905","DOI":"10.1080\/01431161.2019.1698071","article-title":"Optimization of multi-scale segmentation of satellite imagery using fractal geometry using fractal geometry","volume":"41","author":"Karydas","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Gu, H., Han, Y., Yang, Y., Li, H., and Liu, Z. (2018). An Efficient Parallel Multi-Scale Segmentation Method for Remote Sensing Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10040590"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chen, Q., Li, L., Xu, Q., Yang, S., Shi, X., and Liu, X. (2017). Multi-feature segmentation for high-resolution polarimetric sar data based on fractal net evolution approach. Remote Sens., 9.","DOI":"10.3390\/rs9060570"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1080\/19475705.2016.1176605","article-title":"UAV photogrammetry in the post-earthquake scenario: Case studies in L\u2019Aquila","volume":"8","author":"Dominici","year":"2016","journal-title":"Geomat. Nat. Haz. Risk"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"885","DOI":"10.14358\/PERS.77.9.885","article-title":"Automatic structural seismic damage assessment with airborne oblique pictometry imagery","volume":"77","author":"Gerke","year":"2011","journal-title":"Photogramm. Eng. Rem S."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"101169","DOI":"10.1016\/j.ijdrr.2019.101169","article-title":"UAV and GIS based rapid earthquake-induced building damage assessment and methodology for EMS-98 isoseismal map drawing: The June 12, 2017 Mw 6.3 Lesvos (Northeastern Aegean, Greece) earthquake","volume":"37","author":"Mavroulis","year":"2019","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Galarreta, J.F., Kerle, N., and Gerke, M. (2015). UAV-based urban structural damage assessment using object-based image analysis and semantic reasoning. Nat. Hazards Earth Sys., 1087\u20131101.","DOI":"10.5194\/nhess-15-1087-2015"},{"key":"ref_46","unstructured":"Yamazaki, F., Matsuda, T., Denda, S., and Liu, W. (2015, January 6\u20138). Construction of 3D models of buildings damaged by earthquakes using UAV aerial images. Proceedings of the 10th Pacific Conference on Earthquake Engineering, Sydney, Australia."},{"key":"ref_47","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/16\/2621\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:00:33Z","timestamp":1760176833000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/16\/2621"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,13]]},"references-count":47,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["rs12162621"],"URL":"https:\/\/doi.org\/10.3390\/rs12162621","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,13]]}}}