03 Apr 2023
 | 03 Apr 2023
Status: this preprint is open for discussion.

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

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

Abstract. The frequency and intensity of coastal flooding is expected to accelerate in low-elevation coastal areas due to sea level rise. Coastal flooding due to wave runup affects coastal ecosystems and infrastructure, however it can be difficult to monitor in remote and vulnerable areas. Here we use a camera-based system to monitor wave runup as part of the after-storm recovery of an eroded beach on the Texas coast. We analyze high-temporal resolution images of the beach using Convolutional Neural Network (CNN)-based semantic segmentation to study the stochastic properties of runup-driven flooding events. In the first part of this work, we focus on the application of semantic segmentation to identify water and runup events. We train and validate a CNN with over 500 manually classified images, and introduce a post-processing method to reduce false positives. We find that the accuracy of CNN predictions of water pixels is around 90% and strongly depend on the number and diversity of images used for training.

Byungho Kang et al.

Status: open (extended)

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  • RC1: 'Comment on egusphere-2023-231', Anonymous Referee #1, 11 May 2023 reply

Byungho Kang et al.

Byungho Kang et al.


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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.