Abstract

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.

Document type: Article

Full document

The PDF file did not load properly or your web browser does not support viewing PDF files. Download directly to your device: Download PDF document

Original document

The different versions of the original document can be found in:

https://doaj.org/toc/2220-9964 under the license cc-by
https://www.mdpi.com/2220-9964/9/1/14/pdf,
https://doi.org/10.3390/ijgi9010014,
https://dblp.uni-trier.de/db/journals/ijgi/ijgi9.html#KerleNGDV20,
https://research.utwente.nl/en/publications/uav-based-structural-damage-mapping-a-review,
https://ui.adsabs.harvard.edu/abs/2019IJGI....9...14K/abstract,
https://www.narcis.nl/publication/RecordID/oai%3Aris.utwente.nl%3Apublications%2Fef5f7ac9-253c-42f0-a69e-96c0ff17f751,
https://academic.microsoft.com/#/detail/2998460252
http://dx.doi.org/10.3390/ijgi9010014
under the license https://creativecommons.org/licenses/by/4.0/
Back to Top

Document information

Published on 01/01/2019

Volume 2019, 2019
DOI: 10.3390/ijgi9010014
Licence: Other

Document Score

0

Views 0
Recommendations 0

Share this document

claim authorship

Are you one of the authors of this document?