Preprints
https://doi.org/10.5194/egusphere-2023-231
https://doi.org/10.5194/egusphere-2023-231
03 Apr 2023
 | 03 Apr 2023

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.

Journal article(s) based on this preprint

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
Earth Surf. Dynam., 12, 1–10, https://doi.org/10.5194/esurf-12-1-2024,https://doi.org/10.5194/esurf-12-1-2024, 2024
Short summary

Byungho Kang et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-231', Anonymous Referee #1, 11 May 2023
    • AC1: 'Reply on RC1', Orencio Duran Vinent, 28 Jul 2023
  • RC2: 'Comment on egusphere-2023-231', Anonymous Referee #2, 22 Jun 2023
    • AC2: 'Reply on RC2', Orencio Duran Vinent, 28 Jul 2023

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-231', Anonymous Referee #1, 11 May 2023
    • AC1: 'Reply on RC1', Orencio Duran Vinent, 28 Jul 2023
  • RC2: 'Comment on egusphere-2023-231', Anonymous Referee #2, 22 Jun 2023
    • AC2: 'Reply on RC2', Orencio Duran Vinent, 28 Jul 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Orencio Duran Vinent on behalf of the Authors (18 Aug 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (05 Sep 2023) by Sagy Cohen
RR by Anonymous Referee #2 (15 Sep 2023)
RR by Anonymous Referee #1 (24 Oct 2023)
ED: Publish subject to minor revisions (review by editor) (26 Oct 2023) by Sagy Cohen
AR by Orencio Duran Vinent on behalf of the Authors (07 Nov 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (08 Nov 2023) by Sagy Cohen
ED: Publish as is (14 Nov 2023) by Niels Hovius (Editor)
AR by Orencio Duran Vinent on behalf of the Authors (16 Nov 2023)  Manuscript 

Journal article(s) based on this preprint

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
Earth Surf. Dynam., 12, 1–10, https://doi.org/10.5194/esurf-12-1-2024,https://doi.org/10.5194/esurf-12-1-2024, 2024
Short summary

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.