Preprints
https://doi.org/10.5194/nhess-2024-81
https://doi.org/10.5194/nhess-2024-81
30 May 2024
 | 30 May 2024
Status: this preprint is currently under review for the journal NHESS.

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

Abstract. In the wake of a volcanic eruption, the rapid assessment of building damage is paramount for effective response and recovery planning. Uninhabited aerial vehicles, UAVs, offer a unique opportunity for assessing damage after a volcanic eruption, with the ability to collect on demand imagery safely and rapidly from multiple perspectives at high resolutions. In this work, we established a UAV-appropriate tephra fall building damage state framework and used it to label ~50,000 building bounding boxes around ~2,000 individual buildings in 2,811 optical images optical images collected during surveys conducted after the 2021 eruption of La Soufrière volcano, St Vincent and the Grenadines. We used this labelled data to train convolutional neural networks (CNNs) for: 1) Building localisation (average precision = 0.728); 2) Damage classification into two levels of granularity: No Damage vs Damage (F1 score = 0.809); and Moderate damage vs Major damage, (F1 score = 0.838) (1 is the maximum obtainable for both metrics). The trained models were incorporated into a pipeline along with all of the necessary image processing steps to generate spatial data (a shapefile with damage state attributes) for rapid tephra fall building damage mapping. Our pipeline is expected to perform well across other volcanic islands in the Caribbean where building types are similar, though would benefit from additional testing. Through cross validation, we found that the UAV look angle had a minor effect on the performance of damage classification models, while for the building localisation model, the performance was affected by both the look angle and the size of the buildings in images. These observations were used to develop a set of recommendations for data collection during future UAV tephra fall building damage surveys. This is the first attempt to automate tephra fall building damage assessment solely using post-event data. We expect that incorporating additional training data from future eruptions will further refine our model and improve its applicability worldwide. All trained models and pipeline code can be downloaded from GitHub to facilitate collaboration and development.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Eleanor Tennant, Susanna F. Jenkins, Victoria Miller, Richard Robertson, Bihan Wen, Sang-Ho Yun, and Benoit Taisne

Status: open (until 16 Jul 2024)

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Eleanor Tennant, Susanna F. Jenkins, Victoria Miller, Richard Robertson, Bihan Wen, Sang-Ho Yun, and Benoit Taisne
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 vital for response and recovery. Traditional post-event damage assessment methods such as ground surveys, are often time-consuming and resource-intensive, hindering rapid response and recovery efforts. To overcome this, we have developed an automated approach that uses UAV acquired optical images and deep learning to rapidly generate spatial building damage information.
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