Detection of seismic façade damages with multi-temporal oblique aerial imagery
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 a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2020-07-01
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| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/15481603.2020.1768768 |
| _version_ | 1797679090114756608 |
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| author | Diogo Duarte Francesco Nex Norman Kerle George Vosselman |
| author_facet | Diogo Duarte Francesco Nex Norman Kerle George Vosselman |
| author_sort | Diogo Duarte |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T23:09:23Z |
| format | Article |
| id | doaj.art-5a7269867f45434fa7c6c0e673db94be |
| institution | Directory Open Access Journal |
| issn | 1548-1603 1943-7226 |
| language | English |
| last_indexed | 2024-03-11T23:09:23Z |
| publishDate | 2020-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj.art-5a7269867f45434fa7c6c0e673db94be2023-09-21T12:34:16ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262020-07-0157567068610.1080/15481603.2020.17687681768768Detection of seismic façade damages with multi-temporal oblique aerial imageryDiogo Duarte0Francesco Nex1Norman Kerle2George Vosselman3University of TwenteUniversity of TwenteUniversity of TwenteUniversity of TwenteRemote 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.http://dx.doi.org/10.1080/15481603.2020.1768768deep learningchange detectionremote sensingconvolutional neural networkspictometrycnn |
| spellingShingle | Diogo Duarte Francesco Nex Norman Kerle George Vosselman Detection of seismic façade damages with multi-temporal oblique aerial imagery GIScience & Remote Sensing deep learning change detection remote sensing convolutional neural networks pictometry cnn |
| title | Detection of seismic façade damages with multi-temporal oblique aerial imagery |
| title_full | Detection of seismic façade damages with multi-temporal oblique aerial imagery |
| title_fullStr | Detection of seismic façade damages with multi-temporal oblique aerial imagery |
| title_full_unstemmed | Detection of seismic façade damages with multi-temporal oblique aerial imagery |
| title_short | Detection of seismic façade damages with multi-temporal oblique aerial imagery |
| title_sort | detection of seismic facade damages with multi temporal oblique aerial imagery |
| topic | deep learning change detection remote sensing convolutional neural networks pictometry cnn |
| url | http://dx.doi.org/10.1080/15481603.2020.1768768 |
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