Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 24, issue 12
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.
https://doi.org/10.5194/nhess-24-4585-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 24, issue 12
Research article
 | 
12 Dec 2024
Research article |  | 12 Dec 2024

Automating tephra fall building damage assessment using deep learning

Eleanor Tennant, Susanna F. Jenkins, Victoria Miller, Richard Robertson, Bihan Wen, Sang-Ho Yun, and Benoit Taisne

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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.
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