Correcting Global Elevation Models for Canopy and Infrastructure Using a Residual U-Net

Digital Surface Models (DSMs) are commonly employed to investigate topographical characteristics and processes; however, the presence of canopy and infrastructure in urban and forested areas can lead to height biases and inaccuracies. In this study, I aim to correct such biases by applying a deep learning approach known as Residual U-Net to...

Machine learning for post-storm profile predictions: Using XBeach and convolutional neural network structure U-Net to predict 1D dune erosion profile shapes at the Holland Coast

To reduce computational efforts, surrogate models have been developed for dune erosion prediction. Current surrogate models can describe the relationship between the XBeach input and output (Athanasiou, 2022) and provides a prediction of a morphological indicator based on a parameterized input (profile shape parameters and hydrodynamics). In...