Articles | Volume 12, issue 1
https://doi.org/10.5194/esurf-12-1-2024
https://doi.org/10.5194/esurf-12-1-2024
Research article
 | 
03 Jan 2024
Research article |  | 03 Jan 2024

Stochastic properties of coastal flooding events – Part 1: convolutional-neural-network-based semantic segmentation for water detection

Byungho Kang, Rusty A. Feagin, Thomas Huff, and Orencio Durán Vinent

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Earth Surf. Dynam., 12, 105–115, https://doi.org/10.5194/esurf-12-105-2024,https://doi.org/10.5194/esurf-12-105-2024, 2024
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Physical: Geomorphology (including all aspects of fluvial, coastal, aeolian, hillslope and glacial geomorphology)
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Cited articles

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Battjes, J. A.: Surf Similarity, Coastal Engineering Proceedings, 1, 26, https://doi.org/10.9753/icce.v14.26, 1974. a
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Buscombe, D. and Ritchie, A.: Landscape Classification with Deep Neural Networks, Geosciences, 8, 244, https://doi.org/10.3390/geosciences8070244, 2018. a
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A. L.: DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs, arXiv [preprint], https://doi.org/10.48550/arXiv.1606.00915, 2016. a, b
Short summary
Coastal flooding can cause significant damage to coastal ecosystems, infrastructure, and communities and is expected to increase in frequency with the acceleration of sea level rise. In order to respond to it, it is crucial to measure and model their frequency and intensity. Here, we show deep-learning techniques can be successfully used to automatically detect flooding events from complex coastal imagery, opening the way to real-time monitoring and data acquisition for model development.