UAV-based structural damage mapping – Results from 6 years of research in two European projects

N. Kerle*, F. Nex, D. Duarte, A. Vetrivel

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

8 Citations (Scopus)


Structural disaster damage detection and characterisation 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 UAV 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. We have addressed the problem in the context of two European research projects, RECONASS and INACHUS. In this paper we synthesize and evaluate the progress of 6 years of research focused on advanced image analysis that was driven by progress in computer vision, photogrammetry and machine learning, but also by constraints imposed by the needs of first responder and other civil protection end users. The projects focused on damage to individual buildings caused by seismic activity but also explosions, and our work centred on the processing of 3D point cloud information acquired from stereo imagery. Initially focusing on the development of both supervised and unsupervised damage detection methods built on advanced texture features and basic classifiers such as Support Vector Machine and Random Forest, the work moved on to the use of deep learning. In particular the coupling of image-derived features and 3D point cloud information in a Convolutional Neural Network (CNN) proved successful in detecting also subtle damage features. In addition to the detection of standard rubble and debris, CNN-based methods were developed to detect typical façade damage indicators, such as cracks and spalling, including with a focus on multi-temporal and multi-scale feature fusion. We further developed a processing pipeline and mobile app to facilitate near-real time damage mapping. The solutions were tested in a number of pilot experiments and evaluated by a variety of stakeholders.
Original languageEnglish
Title of host publicationGi4DM 2019 – GeoInformation for Disaster Management
Subtitle of host publicationSPRS ICWG III/IVa
EditorsT. Tanzi, O. Altan, M. Chandra, F. Sunar
Number of pages8
Publication statusPublished - 20 Aug 2019
EventGi4DM 2019 – Geoinformation for Disaster Management: ISPRS ICWG III/IVa - Prague, Czech Republic
Duration: 3 Sep 20197 Sep 2019

Publication series

NameInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
ISSN (Print)2194-9034


ConferenceGi4DM 2019 – Geoinformation for Disaster Management
Abbreviated titleGi4DM 2019
Country/TerritoryCzech Republic
Internet address


  • drone
  • computer vision
  • point clouds
  • machine learning
  • CNN
  • first responder


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