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

Viewed

Total article views: 1,613 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,244 281 88 1,613 71 68
  • HTML: 1,244
  • PDF: 281
  • XML: 88
  • Total: 1,613
  • BibTeX: 71
  • EndNote: 68
Views and downloads (calculated since 03 Apr 2023)
Cumulative views and downloads (calculated since 03 Apr 2023)

Viewed (geographical distribution)

Total article views: 1,613 (including HTML, PDF, and XML) Thereof 1,557 with geography defined and 56 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 13 Dec 2024
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.