Articles | Volume 18, issue 6
Nat. Hazards Earth Syst. Sci., 18, 1583–1598, 2018
https://doi.org/10.5194/nhess-18-1583-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Nat. Hazards Earth Syst. Sci., 18, 1583–1598, 2018
https://doi.org/10.5194/nhess-18-1583-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
08 Jun 2018
Research article
| 08 Jun 2018
Usability of aerial video footage for 3-D scene reconstruction and structural damage assessment
Johnny Cusicanqui et al.
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Total article views: 3,070 (including HTML, PDF, and XML)
Cumulative views and downloads
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| HTML | XML | Total | BibTeX | EndNote | |
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| 1,403 | 1,589 | 78 | 3,070 | 62 | 75 |
- HTML: 1,403
- PDF: 1,589
- XML: 78
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- BibTeX: 62
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Total article views: 1,762 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 08 Jun 2018)
| HTML | XML | Total | BibTeX | EndNote | |
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| 1,045 | 654 | 63 | 1,762 | 58 | 62 |
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- PDF: 654
- XML: 63
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- BibTeX: 58
- EndNote: 62
Total article views: 1,308 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 08 Jan 2018)
| HTML | XML | Total | BibTeX | EndNote | |
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| 358 | 935 | 15 | 1,308 | 4 | 13 |
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- Total: 1,308
- BibTeX: 4
- EndNote: 13
Viewed (geographical distribution)
Total article views: 3,070 (including HTML, PDF, and XML)
Thereof 2,770 with geography defined
and 300 with unknown origin.
Total article views: 1,762 (including HTML, PDF, and XML)
Thereof 1,601 with geography defined
and 161 with unknown origin.
Total article views: 1,308 (including HTML, PDF, and XML)
Thereof 1,169 with geography defined
and 139 with unknown origin.
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Cited
12 citations as recorded by crossref.
- Multi-Resolution Feature Fusion for Image Classification of Building Damages with Convolutional Neural Networks D. Duarte et al. 10.3390/rs10101636
- Pre-disaster mapping with drones: an urban case study in Victoria, British Columbia, Canada M. Kucharczyk & C. Hugenholtz 10.5194/nhess-19-2039-2019
- Detection of Disaster-Affected Cultural Heritage Sites from Social Media Images Using Deep Learning Techniques P. Kumar et al. 10.1145/3383314
- Use of UAV-based photogrammetry products for semi-automatic detection and classification of asphalt road damage in landslide-affected areas N. Nappo et al. 10.1016/j.enggeo.2021.106363
- Towards Real-Time Building Damage Mapping with Low-Cost UAV Solutions F. Nex et al. 10.3390/rs11030287
- Sparse and Low-Overlapping Point Cloud Registration Network for Indoor Building Environments Z. Zhang et al. 10.1061/(ASCE)CP.1943-5487.0000959
- UAV-Based Structural Damage Mapping: A Review N. Kerle et al. 10.3390/ijgi9010014
- Earthquake Reconnaissance Data Sources, a Literature Review D. Contreras et al. 10.3390/earth2040060
- Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review S. Ghaffarian et al. 10.3390/rs10111760
- Integrating UAV and Ground Panoramic Images for Point Cloud Analysis of Damaged Building J. Jhan et al. 10.1109/LGRS.2020.3048150
- Utility Based Scheduling for Multi-UAV Search Systems in Disaster-Hit Areas K. Miyano et al. 10.1109/ACCESS.2019.2900865
- Detection of seismic façade damages with multi-temporal oblique aerial imagery D. Duarte et al. 10.1080/15481603.2020.1768768
11 citations as recorded by crossref.
- Multi-Resolution Feature Fusion for Image Classification of Building Damages with Convolutional Neural Networks D. Duarte et al. 10.3390/rs10101636
- Pre-disaster mapping with drones: an urban case study in Victoria, British Columbia, Canada M. Kucharczyk & C. Hugenholtz 10.5194/nhess-19-2039-2019
- Detection of Disaster-Affected Cultural Heritage Sites from Social Media Images Using Deep Learning Techniques P. Kumar et al. 10.1145/3383314
- Use of UAV-based photogrammetry products for semi-automatic detection and classification of asphalt road damage in landslide-affected areas N. Nappo et al. 10.1016/j.enggeo.2021.106363
- Towards Real-Time Building Damage Mapping with Low-Cost UAV Solutions F. Nex et al. 10.3390/rs11030287
- Sparse and Low-Overlapping Point Cloud Registration Network for Indoor Building Environments Z. Zhang et al. 10.1061/(ASCE)CP.1943-5487.0000959
- UAV-Based Structural Damage Mapping: A Review N. Kerle et al. 10.3390/ijgi9010014
- Earthquake Reconnaissance Data Sources, a Literature Review D. Contreras et al. 10.3390/earth2040060
- Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review S. Ghaffarian et al. 10.3390/rs10111760
- Integrating UAV and Ground Panoramic Images for Point Cloud Analysis of Damaged Building J. Jhan et al. 10.1109/LGRS.2020.3048150
- Utility Based Scheduling for Multi-UAV Search Systems in Disaster-Hit Areas K. Miyano et al. 10.1109/ACCESS.2019.2900865
1 citations as recorded by crossref.
Latest update: 04 Dec 2022
Short summary
Aerial multi-perspective images can be used for the effective assessment of post-disaster structural damage. Alternatively, rapidly available video data can be processed for the same purpose. However, video quality characteristics are different than those of images taken with still cameras. The use of video data in post-disaster damage assessment has not been demonstrated. Based on a comparative assessment, our findings support the application of video data in post-disaster damage assessment.
Aerial multi-perspective images can be used for the effective assessment of post-disaster...
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