Articles | Volume 12, issue 1
https://doi.org/10.5194/esurf-12-1-2024
© Author(s) 2024. This work is distributed under the Creative Commons Attribution 4.0 License.
Stochastic properties of coastal flooding events – Part 1: convolutional-neural-network-based semantic segmentation for water detection
Related authors
Cited articles
Alvarez-Ellacuria, A., Orfila, A., Gómez-Pujol, L., Simarro, G., and Obregon, N.: Decoupling spatial and temporal patterns in short-term beach shoreline response to wave climate, Geomorphology, 128, 199–208, https://doi.org/10.1016/j.geomorph.2011.01.008, 2011. a
Battjes, J. A.: Surf Similarity, Coastal Engineering Proceedings, 1, 26, https://doi.org/10.9753/icce.v14.26, 1974. a
Bengio, Y.: Deep Learning of Representations for Unsupervised and Transfer Learning, Proceedings of Machine Learning Research, 27, 17–36, http://proceedings.mlr.press/v27/bengio12a.html (last access: 20 December 2023), 2012. a
Buscombe, D. and Ritchie, A.: Landscape Classification with Deep Neural Networks, Geosciences, 8, 244, https://doi.org/10.3390/geosciences8070244, 2018. a
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A. L.: DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs, arXiv [preprint], https://doi.org/10.48550/arXiv.1606.00915, 2016. a, b