Detection of seismic façade damages with multi-temporal oblique aerial imagery

D. Duarte*, F. Nex, N. Kerle, G. Vosselman

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

8 Citations (Scopus)
58 Downloads (Pure)


Remote sensing images have long been recognized as useful for the detection of building damages, mainly due to their wide coverage, revisit capabilities and high spatial resolution. The majority of contributions aimed at identifying debris and rubble piles, as the main focus is to assess collapsed and partially collapsed structures. However, these approaches might not be optimal for the image classification of façade damages, where damages might appear in the form of spalling, cracks and collapse of small segments of the façade. A few studies focused their damage detection on the façades using only post-event images. Nonetheless, several studies achieved better performances in damage detection approaches when considering multi-temporal image data. Hence, in this work a multi-temporal façade damage detection is tested. The first objective is to optimally merge pre- and post-event aerial oblique imagery within a supervised classification approach using convolutional neural networks to detect façade damages. The second objective is related to the fact that façades are normally depicted in several views in aerial manned photogrammetric surveys; hence, different procedures combining these multi-view image data are also proposed and embedded in the image classification approach. Six multi-temporal approaches are compared against 3 mono-temporal ones. The results indicate the superiority of multi-temporal approaches (up to ~25% in f1-score) when compared to the mono-temporal ones. The best performing multi-temporal approach takes as input sextuples (3 views per epoch, per façade) within a late fusion approach to perform the image classification of façade damages. However, the detection of small damages, such as smaller cracks or smaller areas of spalling, remains challenging in this approach, mainly due to the low resolution (~0.14 m ground sampling distance) of the dataset used.
Original languageEnglish
Pages (from-to)670-686
Number of pages17
JournalGIScience & remote sensing
Issue number5
Publication statusPublished - 25 May 2020


  • Deep learning
  • Change detection
  • Remote Sensing
  • convolutional neural networks
  • Pictometry
  • CNN
  • UT-Hybrid-D


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