Articles | Volume 18, issue 6
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.
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
| 08 Jun 2018
Usability of aerial video footage for 3-D scene reconstruction and structural damage assessment
Johnny Cusicanqui
CORRESPONDING AUTHOR
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, the Netherlands
Norman Kerle
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, the Netherlands
Francesco Nex
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, the Netherlands
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Total article views: 3,870 (including HTML, PDF, and XML)
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Total article views: 2,495 (including HTML, PDF, and XML)
Thereof 2,294 with geography defined
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Total article views: 1,375 (including HTML, PDF, and XML)
Thereof 1,230 with geography defined
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Cited
18 citations as recorded by crossref.
- Detection of Disaster-Affected Cultural Heritage Sites from Social Media Images Using Deep Learning Techniques P. Kumar et al. 10.1145/3383314
- Efficient building damage assessment from post-disaster aerial video using lightweight deep learning models C. Liu et al. 10.1080/01431161.2023.2277163
- Sparse and Low-Overlapping Point Cloud Registration Network for Indoor Building Environments Z. Zhang et al. 10.1061/(ASCE)CP.1943-5487.0000959
- Earthquake Reconnaissance Data Sources, a Literature Review D. Contreras et al. 10.3390/earth2040060
- 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
- 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
- Efficient post-earthquake reconnaissance planning using adaptive batch-mode active learning A. Cheraghi et al. 10.1016/j.aei.2024.102414
- Post-disaster damage and loss assessment in the Iranian healthcare sector: a qualitative interview study J. Miri et al. 10.1186/s12889-024-19877-w
- Real-time autonomous indoor navigation and vision-based damage assessment of reinforced concrete structures using low-cost nano aerial vehicles S. Tavasoli et al. 10.1016/j.jobe.2023.106193
- 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
- UAV-Based Structural Damage Mapping: A Review N. Kerle et al. 10.3390/ijgi9010014
- Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review S. Ghaffarian et al. 10.3390/rs10111760
- Systematic assessment method for post-earthquake damage of regional buildings using adaptive-network-based fuzzy inference system W. Zhang et al. 10.1016/j.jobe.2023.107682
- Structural Building Damage Detection with Deep Learning: Assessment of a State-of-the-Art CNN in Operational Conditions F. Nex et al. 10.3390/rs11232765
- Detection of seismic façade damages with multi-temporal oblique aerial imagery D. Duarte et al. 10.1080/15481603.2020.1768768
16 citations as recorded by crossref.
- Detection of Disaster-Affected Cultural Heritage Sites from Social Media Images Using Deep Learning Techniques P. Kumar et al. 10.1145/3383314
- Efficient building damage assessment from post-disaster aerial video using lightweight deep learning models C. Liu et al. 10.1080/01431161.2023.2277163
- Sparse and Low-Overlapping Point Cloud Registration Network for Indoor Building Environments Z. Zhang et al. 10.1061/(ASCE)CP.1943-5487.0000959
- Earthquake Reconnaissance Data Sources, a Literature Review D. Contreras et al. 10.3390/earth2040060
- 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
- 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
- Efficient post-earthquake reconnaissance planning using adaptive batch-mode active learning A. Cheraghi et al. 10.1016/j.aei.2024.102414
- Post-disaster damage and loss assessment in the Iranian healthcare sector: a qualitative interview study J. Miri et al. 10.1186/s12889-024-19877-w
- Real-time autonomous indoor navigation and vision-based damage assessment of reinforced concrete structures using low-cost nano aerial vehicles S. Tavasoli et al. 10.1016/j.jobe.2023.106193
- 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
- UAV-Based Structural Damage Mapping: A Review N. Kerle et al. 10.3390/ijgi9010014
- Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review S. Ghaffarian et al. 10.3390/rs10111760
- Systematic assessment method for post-earthquake damage of regional buildings using adaptive-network-based fuzzy inference system W. Zhang et al. 10.1016/j.jobe.2023.107682
2 citations as recorded by crossref.
Latest update: 19 Nov 2024
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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