{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T04:28:08Z","timestamp":1771388888194,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,2,1]],"date-time":"2019-02-01T00:00:00Z","timestamp":1548979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The timely and efficient generation of detailed damage maps is of fundamental importance following disaster events to speed up first responders\u2019 (FR) rescue activities and help trapped victims. Several works dealing with the automated detection of building damages have been published in the last decade. The increasingly widespread availability of inexpensive UAV platforms has also driven their recent adoption for rescue operations (i.e., search and rescue). Their deployment, however, remains largely limited to visual image inspection by skilled operators, limiting their applicability in time-constrained real conditions. This paper proposes a new solution to autonomously map building damages with a commercial UAV in near real-time. The solution integrates different components that allow the live streaming of the images on a laptop and their processing on the fly. Advanced photogrammetric techniques and deep learning algorithms are combined to deliver a true-orthophoto showing the position of building damages, which are already processed by the time the UAV returns to base. These algorithms have been customized to deliver fast results, fulfilling the near real-time requirements. The complete solution has been tested in different conditions, and received positive feedback by the FR involved in the EU funded project INACHUS. Two realistic pilot tests are described in the paper. The achieved results show the great potential of the presented approach, how close the proposed solution is to FR\u2019 expectations, and where more work is still needed.<\/jats:p>","DOI":"10.3390\/rs11030287","type":"journal-article","created":{"date-parts":[[2019,2,1]],"date-time":"2019-02-01T11:19:58Z","timestamp":1549019998000},"page":"287","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":93,"title":["Towards Real-Time Building Damage Mapping with Low-Cost UAV Solutions"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5712-6902","authenticated-orcid":false,"given":"Francesco","family":"Nex","sequence":"first","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 AE Enschede, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1599-8956","authenticated-orcid":false,"given":"Diogo","family":"Duarte","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 AE Enschede, The Netherlands"}]},{"given":"Anne","family":"Steenbeek","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 AE Enschede, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4513-4681","authenticated-orcid":false,"given":"Norman","family":"Kerle","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 AE Enschede, The Netherlands"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,1]]},"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","doi-asserted-by":"crossref","unstructured":"Showalter, P.S., and Lu, Y. (2009). Utilizing new technologies in managing hazards and disasters. Geospatial Techniques in Urban Hazard and Disaster Analysis, Springer.","DOI":"10.1007\/978-90-481-2238-7"},{"key":"ref_3","unstructured":"(2018, December 31). United Nations INSARAG Guidelines. Available online: https:\/\/bit.ly\/2HHLcoX."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1522","DOI":"10.1080\/00045608.2013.784098","article-title":"Capturing damage assessment with a spatial video: An example of a building and street-scale analysis of tornado-related mortality in Joplin, Missouri, 2011","volume":"103","author":"Curtis","year":"2013","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_5","unstructured":"Ishii, M., Goto, T., Sugiyama, T., Saji, H., and Abe, K. (2002, January 22\u201325). Detection of earthquake damaged areas from aerial photographs by using color and edge information. Proceedings of the 5th Asian Conference on Computer Vision, Melbourne, Australia."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1193\/1.2101127","article-title":"Detection and animation of damage using very high-resolution satellite data following the 2003 Bam, Iran, earthquake","volume":"21","author":"Vu","year":"2005","journal-title":"Earthq. Spectra"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3369","DOI":"10.1080\/01431161003727671","article-title":"Building-damage detection using post-seismic high-resolution SAR satellite data","volume":"31","author":"Balz","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Brunner, D., Schulz, K., and Brehm, T. (2011, January 11\u201313). Building damage assessment in decimeter resolution SAR imagery: A future perspective. Proceedings of the Joint Urban Remote Sensing Event, Munich, Germany.","DOI":"10.1109\/JURSE.2011.5764759"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3037","DOI":"10.1016\/j.jas.2010.06.031","article-title":"Terrestrial laser scanning intensity data applied to damage detection for historical buildings","volume":"37","year":"2010","journal-title":"J. Archaeol. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1258","DOI":"10.1109\/LGRS.2013.2257676","article-title":"Segment-based classification of damaged building roofs in aerial laser scanning data","volume":"10","author":"Khoshelham","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","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\u00a9 imagery","volume":"77","author":"Gerke","year":"2011","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_12","unstructured":"(2018, December 30). Copernicus Emergency Management Service. Available online: https:\/\/bit.ly\/2Giqg5H."},{"key":"ref_13","first-page":"466","article-title":"Satellite-based damage mapping following the 2006 Indonesia earthquake\u2014How accurate was it?","volume":"12","author":"Kerle","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.isprsjprs.2017.03.001","article-title":"Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning","volume":"140","author":"Vetrivel","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Duarte, D., Nex, F., Kerle, N., and Vosselman, G. (2017, January 4\u20137). Towards a more efficient detection of earthquake induced facade damages using oblique UAV imagery. Proceedings of the ISPRS\u2014International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Bonn, Germany.","DOI":"10.5194\/isprs-archives-XLII-2-W6-93-2017"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1080\/22797254.2018.1458584","article-title":"Contextual classification using photometry and elevation data for damage detection after an earthquake event","volume":"51","author":"Rupnik","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.cageo.2014.04.001","article-title":"Development of an UAS for post-earthquake disaster surveying and its application in Ms7.0 Lushan Earthquake, Sichuan, China","volume":"68","author":"Xu","year":"2014","journal-title":"Comput. Geosci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"15717","DOI":"10.3390\/s150715717","article-title":"UAV deployment exercise for mapping purposes: evaluation of emergency response applications","volume":"15","author":"Boccardo","year":"2015","journal-title":"Sensors"},{"key":"ref_19","unstructured":"(2018, December 31). INACHUS Project. Available online: www.inachus.eu."},{"key":"ref_20","unstructured":"Palossi, D., Loquercio, A., Conti, F., Flamand, E., Scaramuzza, D., and Benini, L. (arXiv, 2018). Ultra low power deep-learning-powered autonomous nano drones, arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1007\/978-3-319-67361-5_25","article-title":"Mapping on the fly: Real-time 3D dense reconstruction, digital surface map and incremental orthomosaic generation for unmanned aerial vehicles","volume":"Volume 5","author":"Hutter","year":"2018","journal-title":"Field and Service Robotics"},{"key":"ref_22","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the CVPR, Las Vegas, NV, USA."},{"key":"ref_23","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very deep convolutional networks for large-scale image recognition. Proceedings of the ICLR, San Diego, CA, USA."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TGRS.2016.2612821","article-title":"Convolutional neural networks for large-scale remote-sensing image classification","volume":"55","author":"Maggiori","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Fu, G., Liu, C., Zhou, R., Sun, T., and Zhang, Q. (2017). Classification for high resolution remote sensing imagery using a fully convolutional network. Remote Sens., 9.","DOI":"10.3390\/rs9050498"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1109\/LGRS.2017.2672734","article-title":"Road structure refined CNN for road extraction in aerial images","volume":"14","author":"Wei","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/s41074-017-0027-2","article-title":"Visual SLAM algorithms: A survey from 2010 to 2016","volume":"9","author":"Taketomi","year":"2017","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1109\/TRO.2016.2624754","article-title":"Past, present, and future of simultaneous localization and mapping: Towards the robust-perception age","volume":"32","author":"Cadena","year":"2016","journal-title":"IEEE Trans. Robot."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1080\/10095020.2017.1420509","article-title":"A survey on vision-based UAV navigation","volume":"21","author":"Lu","year":"2018","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.imavis.2012.02.009","article-title":"Visual SLAM: Why filter?","volume":"30","author":"Strasdat","year":"2012","journal-title":"Image Vis. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Schonberger, J.L., and Frahm, J.-M. (26\u20131, January 26). Structure-from-Motion revisited. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.445"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Klein, G., and Murray, D. (2007, January 13\u201316). Parallel tracking and mapping for small AR workspaces. Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, Nara, Japan.","DOI":"10.1109\/ISMAR.2007.4538852"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wu, C. (July, January 29). Towards linear-time incremental structure from motion. Proceedings of the 2013 International Conference on 3D Vision\u20143DV 2013, Seattle, WA, USA.","DOI":"10.1109\/3DV.2013.25"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kendall, A., Martirosyan, H., Dasgupta, S., Henry, P., Kennedy, R., Bachrach, A., and Bry, A. (arXiv, 2017). End-to-end learning of geometry and context for deep stereo regression, arXiv.","DOI":"10.1109\/ICCV.2017.17"},{"key":"ref_36","unstructured":"(2018, December 31). DroneDeploy. Available online: https:\/\/bit.ly\/2S2wTzQ."},{"key":"ref_37","unstructured":"(2018, December 31). LiveDroneMap. Available online: http:\/\/www.gaia3d.com\/en\/?p=2304."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.5194\/nhess-18-1583-2018","article-title":"Usability of aerial video footage for 3D-scene reconstruction and structural damage assessment","volume":"18","author":"Cusicanqui","year":"2018","journal-title":"Nat. Hazards Earth Syst. Sci. Discuss."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"25","DOI":"10.14358\/PERS.73.1.25","article-title":"New methodologies for true orthophoto generation","volume":"73","author":"Habib","year":"2007","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.3390\/rs2102369","article-title":"Applicability of green-red vegetation index for remote sensing of vegetation phenology","volume":"2","author":"Motohka","year":"2010","journal-title":"Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3747","DOI":"10.1109\/TGRS.2010.2048116","article-title":"Morphological attribute profiles for the analysis of very high resolution images","volume":"48","author":"Benediktsson","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","unstructured":"(2018, December 31). Matworks & Deep Learning. Available online: https:\/\/bit.ly\/2Wy3T1K."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/3\/287\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:30:14Z","timestamp":1760185814000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/3\/287"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,1]]},"references-count":42,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["rs11030287"],"URL":"https:\/\/doi.org\/10.3390\/rs11030287","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,1]]}}}