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UAV-Based Structural Damage Mapping: A Review

Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 AE Enschede, The Netherlands
Technische Universität Braunschweig, Institut für Geodäsie und Photogrammetrie, Bienroder Weg 81, 38106 Braunschweig, Germany
Department of Mathematics, University of Coimbra, Apartado 3008 EC Santa Cruz, 3001-501 Coimbra, Portugal
Institute for Systems Engineering and Computers, University of Coimbra, Rua Sílvio Lima, Pólo II, 3030-290 Coimbra, Portugal
Experian Singapore Pte. Ltd., 10 Kallang Ave #14-18 Aperia Tower 2, Singapore 339510, Singapore
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(1), 14;
Received: 22 November 2019 / Revised: 16 December 2019 / Accepted: 23 December 2019 / Published: 26 December 2019
(This article belongs to the Special Issue GI for Disaster Management)
Structural disaster damage detection and characterization is one of the oldest remote sensing challenges, and the utility of virtually every type of active and passive sensor deployed on various air- and spaceborne platforms has been assessed. The proliferation and growing sophistication of unmanned aerial vehicles (UAVs) in recent years has opened up many new opportunities for damage mapping, due to the high spatial resolution, the resulting stereo images and derivatives, and the flexibility of the platform. This study provides a comprehensive review of how UAV-based damage mapping has evolved from providing simple descriptive overviews of a disaster science, to more sophisticated texture and segmentation-based approaches, and finally to studies using advanced deep learning approaches, as well as multi-temporal and multi-perspective imagery to provide comprehensive damage descriptions. The paper further reviews studies on the utility of the developed mapping strategies and image processing pipelines for first responders, focusing especially on outcomes of two recent European research projects, RECONASS (Reconstruction and Recovery Planning: Rapid and Continuously Updated Construction Damage, and Related Needs Assessment) and INACHUS (Technological and Methodological Solutions for Integrated Wide Area Situation Awareness and Survivor Localization to Support Search and Rescue Teams). Finally, recent and emerging developments are reviewed, such as recent improvements in machine learning, increasing mapping autonomy, damage mapping in interior, GPS-denied environments, the utility of UAVs for infrastructure mapping and maintenance, as well as the emergence of UAVs with robotic abilities. View Full-Text
Keywords: drone; computer vision; point clouds; machine learning; CNN; GAN; first responder; RECONASS; INACHUS drone; computer vision; point clouds; machine learning; CNN; GAN; first responder; RECONASS; INACHUS
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MDPI and ACS Style

Kerle, N.; Nex, F.; Gerke, M.; Duarte, D.; Vetrivel, A. UAV-Based Structural Damage Mapping: A Review. ISPRS Int. J. Geo-Inf. 2020, 9, 14.

AMA Style

Kerle N, Nex F, Gerke M, Duarte D, Vetrivel A. UAV-Based Structural Damage Mapping: A Review. ISPRS International Journal of Geo-Information. 2020; 9(1):14.

Chicago/Turabian Style

Kerle, Norman, Francesco Nex, Markus Gerke, Diogo Duarte, and Anand Vetrivel. 2020. "UAV-Based Structural Damage Mapping: A Review" ISPRS International Journal of Geo-Information 9, no. 1: 14.

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