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

Abstract

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.

The Application of CNN-Based Image Segmentation for Tracking Coastal Erosion and Post-Storm Recovery

“…We chose the Deeplab v3+ architecture, which incorporates a Resnet-18 model pretrained on ImageNet, to form the backbone of our CNN analysis for semantic segmentation. Of the 156 high-quality images, we used 132 to train Resnet-18, following previous studies that indicated over 100 images sufficient for reliable semantic segmentation prediction via transfer learning [19].…”