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This content will become publicly available on September 1, 2025

Title: A new framework for quantifying alongshore variability of swash motion using fully convolutional networks
Waves running up and down the beach (‘swash’) at the landward edge of the ocean can cause changes to the beach topology, can erode dunes, and can result in inland flooding. Despite the importance of swash, field observations are difficult to obtain in the thin, bubbly, and potentially sediment laden fluid layers. Here, swash excursions along an Atlantic Ocean beach are estimated with a new framework, V-BeachNet, that uses a fully convolutional network to distinguish between sand and the moving edge of the wave in rapid sequences of images. V-BeachNet is trained with 16 randomly selected and manually segmented images of the swash zone, and is used to estimate swash excursions along 200 m of the shoreline by automatically segmenting four 1-h sequences of images that span a range of incident wave conditions. Data from a scanning lidar system are used to validate the swash estimates along a cross-shore transect within the camera field of view. V-BeachNet estimates of swash spectra, significant wave heights, and wave-driven setup (increases in the mean water level) agree with those estimated from the lidar data.  more » « less
Award ID(s):
2318785 1848650 2044850 2052443
PAR ID:
10513109
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
Elsevier
Date Published:
Journal Name:
Coastal Engineering
Edition / Version:
1
Volume:
192
Issue:
C
ISSN:
0378-3839
Page Range / eLocation ID:
104542
Subject(s) / Keyword(s):
Computer vision Machine learning Fully convolutional networks Swash Runup Alongshore variation
Format(s):
Medium: X Size: 10.4MB Other: pdf
Size(s):
10.4MB
Sponsoring Org:
National Science Foundation
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