Articles | Volume 24, issue 12
https://doi.org/10.5194/nhess-24-4585-2024
© Author(s) 2024. 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-24-4585-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Dec 2024
Research article |
| 12 Dec 2024
| 12 Dec 2024
Automating tephra fall building damage assessment using deep learning
Earth Observatory of Singapore, Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore, 639798, Singapore
Susanna F. Jenkins
Earth Observatory of Singapore, Asian School of the Environment, Nanyang Technological University, Singapore, 639798, Singapore
Victoria Miller
GNS Science, P.O. Box 30368, 5040 Lower Hutt, Aotearoa / New Zealand
Richard Robertson
The UWI Seismic Research Centre, Saint Augustine, Trinidad and Tobago
Bihan Wen
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore
Sang-Ho Yun
Earth Observatory of Singapore, Asian School of the Environment, Nanyang Technological University, Singapore, 639798, Singapore
Benoit Taisne
Earth Observatory of Singapore, Asian School of the Environment, Nanyang Technological University, Singapore, 639798, Singapore
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Short summary
After a volcanic eruption, assessing building damage quickly is important for responding to and recovering from the disaster. Traditional damage assessment methods such as ground surveys can be time-consuming and resource-intensive, hindering rapid response and recovery efforts. To overcome this, we have developed an automated approach for tephra fall building damage assessment. Our approach uses drone-acquired optical images and deep learning to rapidly generate building damage data.
After a volcanic eruption, assessing building damage quickly is important for responding to and...
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