Infrastructure degradation and post-disaster damage detection using anomaly detecting Generative Adversarial Networks

S. M. Tilon*, F. Nex, D. Duarte, N. Kerle, G. Vosselman

*Corresponding author for this work

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Abstract

Degradation and damage detection provides essential information to maintenance workers in routine monitoring and to first responders in post-disaster scenarios. Despite advance in Earth Observation (EO), image analysis and deep learning techniques, the quality and quantity of training data for deep learning is still limited. As a result, no robust method has been found yet that can transfer and generalize well over a variety of geographic locations and typologies of damages. Since damages can be seen as anomalies, occurring sparingly over time and space, we propose to use an anomaly detecting Generative Adversarial Network (GAN) to detect damages. The main advantages of using GANs are that only healthy unannotated images are needed, and that a variety of damages, including the never before seen damage, can be detected. In this study we aimed to investigate 1) the ability of anomaly detecting GANs to detect degradation (potholes and cracks) in asphalt road infrastructures using Mobile Mapper imagery and building damage (collapsed buildings, rubble piles) using post-disaster aerial imagery, and 2) the sensitivity of this method against various types of pre-processing. Our results show that we can detect damages in urban scenes at satisfying levels but not on asphalt roads. Future work will investigate how to further classify the found damages and how to improve damage detection for asphalt roads.
Original languageEnglish
Title of host publicationISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Subtitle of host publicationXXIV ISPRS Congress
EditorsN. Paparoditis, C. Mallet, F. Lafarge, F. Remondino, I. Toschi, T. Fuse
PublisherInternational Society for Photogrammetry and Remote Sensing (ISPRS)
Pages573-582
Number of pages10
VolumeV-2-2020
DOIs
Publication statusPublished - 3 Aug 2020
EventXXIVth ISPRS Congress 2020 - Nice-Acropolis Congress and Exhibition Centre, Nice, France
Duration: 4 Jul 202010 Jul 2020
Conference number: 24
http://www.isprs2020-nice.com

Publication series

NameISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
PublisherCopernicus
ISSN (Print)2194-9042

Conference

ConferenceXXIVth ISPRS Congress 2020
Abbreviated titleISPRS 2020
CountryFrance
CityNice
Period4/07/2010/07/20
Internet address

Keywords

  • Generative adversarial networks
  • anomaly detection
  • degradation
  • damage
  • infrastructure monitoring
  • post-disaster

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    Tilon, S. M., Nex, F., Duarte, D., Kerle, N., & Vosselman, G. (2020). Infrastructure degradation and post-disaster damage detection using anomaly detecting Generative Adversarial Networks. In N. Paparoditis, C. Mallet, F. Lafarge, F. Remondino, I. Toschi, & T. Fuse (Eds.), ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences: XXIV ISPRS Congress (Vol. V-2-2020, pp. 573-582). (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences). International Society for Photogrammetry and Remote Sensing (ISPRS). https://doi.org/10.5194/isprs-annals-V-2-2020-573-2020