Automating tephra fall building damage assessment using deep learning

• Published in: Natural hazards and earth system sciences 2024-12, Vol.24 (12), p.4585-4608
• Main Authors: Tennant, Eleanor, Jenkins, Susanna F, Miller, Victoria, Robertson, Richard, Wen, Bihan, Yun, Sang-Ho, Taisne, Benoit
• Format: Article
• Language: English
• Subjects: Aerial surveys; Analysis; Artificial neural networks; Automation; Building damage; Buildings; Classification; Damage; Damage assessment; Damage detection; Data collection; Deep learning; Disaster recovery; Earthquakes; Eruptions; Geospatial data; Hurricanes; Image processing; Information processing; Localization; Machine learning; Mechanization; Neural networks; Real property; Recovery plans; Seismic engineering; Spatial data; Storm damage; Surveys; Tephra; Valuation; Volcanic activity; Volcanic eruptions; Volcanic islands; Volcanoes
• ISSN: 1684-9981; 1561-8633; 1684-9981
• DOI: 10.5194/nhess-24-4585-2024

In the wake of a volcanic eruption, the rapid assessment of building damage is paramount for effective response and recovery planning. Uncrewed 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 ∼2000 individual buildings in 2811 optical images collected during surveys conducted after the 2021 eruption of La Soufrière volcano, St Vincent and the Grenadines. We used these labelled data to train convolutional neural networks (CNNs) for (1) building localisation (average precision equals 0.728) and (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 the necessary image processing steps to generate spatial data (a georeferenced vector with damage state attributes) for rapid tephra fall building damage mapping. Using our pipeline, we assessed tephra fall building damage for the town of Owia, finding that 22 % of buildings that received 50–90 mm of tephra accumulation experienced at least moderate damage. The pipeline is expected to perform well across other volcanic islands in the Caribbean where building types are similar, though it 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. To facilitate continued development and collaboration all trained models and the pipeline code can be downloaded from GitHub.