Ocean Waves and Remote Sensing

Neueste Paper

• On the Limits of Univariate Deep Learning for Significant Wave Height Forecasting

  Yilin Zhai, Hongyuan Shi, Zaijin You · 28. September 2026

  This study conducts a systematic hyperparameter search across five deep learning architectures, DLinear, LSTM, PatchTST, ResAttLstm, and Mamba2, and nine context lengths (1-168 h) for single-station significant wave height (Hs) forecasting on NDBC buoy 41009, followed by re-evaluation of the best co…

• AUWave: A Data-Driven Model for Reconstructing Significant Wave Heights Using Sparse Observations

  Hongyuan Shi, Yilin Zhai, Ping Dong, Zaijin You, Chao Zhan, Qing Wang · 28. September 2026

  Reconstructing high-resolution regional significant wave height (SWH) fields from sparse buoy observations is a critical challenge for ocean monitoring. We introduce AUWave, a hybrid deep learning framework that fuses a station-wise encoder with a multi-scale U-Net enhanced by self-attention to reco…

• DU-NO: A Parameter-Efficient Double U-Shaped Neural Operator for Phase-Resolving Wave Modeling

  Enrique Hernandez Noguera, Md Meftahul Ferdaus, Nathan Cooper, Elias Ioup, Mahdi Abdelguerfi · 14. September 2026

  Phase-resolving wave models such as FUNWAVE-TVD are the accuracy standard for nearshore dynamics, resolving the shoaling, refraction, and breaking of individual waves, but their cost rules them out for the ensembles, uncertainty quantification, and real-time warning that operational forecasting dema…

• MorphoGP: A Nonparametric Framework for Predicting Equilibrium Beach Profiles Under Tidal Influence

  Xi Wu, Yanqing Wei, Hang Yin, Pengze Li, Hongshuai Qi, Xi Chen · 20. August 2026

  The prediction of equilibrium beach profiles under tidal influence is of fundamental importance for sustainable coastal development, informing shoreline protection strategies and managing coastal ecosystems under changing environmental conditions. However, it remains challenging due to the highly no…

• Developing an Offshore Machine Learning Surface Layer Scheme

  Susan Dettling, Sue Ellen Haupt, Thomas Brummet, Patrick Hawbecker, Branko Kosović, David John Gagne · 18. August 2026

  Turbulent fluxes between the surface and the atmosphere are typically parameterized using empirically fit relationships. Here we test machine learning techniques for fitting the relationship for the offshore environment. To do that, data from three offshore sites are used: the Martha's Vineyard Coas…

• HPC-Enabled Video-based Coastal Wave Parameter Estimation Using V-JEPA and Deep Spatiotemporal Learning

  Abubakar Hamisu Kamagata, Dharm Singh Jat, Attlee Munyaradzi Gamundani, Saravanakumar Paramasivam, Babangida Sani, Aliyu Zakariyya · 15. Juli 2026

  High deployment cost, poor spatial coverage and susceptibility to storm conditions are all challenges faced by traditional in-situ methods. This paper presents a video-based and high performance computing (HPC) enabled deep learning framework for joint sensor free estimation of five coastal wave par…

• Sampling sea state using a diffusion model

  Jiarong Wu, Bertrand Chapron, Laure Zanna · 26. Juni 2026

  Sea state prediction is essential for operational maritime applications and coupled earth system modeling, yet current spectral wave models remain computationally prohibitive for many use cases, including online coupling to climate simulations and making probabilistic (ensemble-based) predictions. W…

• Physics-Guided Spatiotemporal Learning for Coastal Wave Peak Period Estimation from Video

  Abubakar Hamisu Kamagata, Dharm Singh Jat, Attlee Munyaradzi Gamundani, Abhishek Srivastava, Paramasivam Saravanakumar · 12. Juni 2026

  Wave parameters in the nearshore are crucial for coastal engineering, shoreline protection, marine hazard assessment, and coastal management for climate resilience. Traditional monitoring systems like buoys and radar platforms offer accurate monitoring but can have high installation and maintenance …

• Operator Learning for Surrogate Modeling of Wave-Induced Forces from Sea Surface Waves

  Shukai Cai, Sourav Dutta, Mark Loveland, Eirik Valseth, Peter Rivera-Casillas, Corey Trahan, Clint Dawson · 9. April 2026

  Wave setup plays a significant role in transferring wave-induced energy to currents and causing an increase in water elevation. This excess momentum flux, known as radiation stress, motivates the coupling of circulation models with wave models to improve the accuracy of storm surge prediction, howev…

• OceanSAR-2: A Universal Feature Extractor for SAR Ocean Observation

  Alexandre Tuel, Thomas Kerdreux, Quentin Febvre, Alexis Mouche, Antoine Grouazel, Jean-Renaud Miadana, Antoine Audras, Chen Wang, Bertrand Chapron · 13. Januar 2026

  We present OceanSAR-2, the second generation of our foundation model for SAR-based ocean observation. Building on our earlier release, which pioneered self-supervised learning on Sentinel-1 Wave Mode data, OceanSAR-2 relies on improved SSL training and dynamic data curation strategies, which enhance…

• NASTaR: NovaSAR Automated Ship Target Recognition Dataset

  Benyamin Hosseiny, Kamirul Kamirul, Odysseas Pappas, Alin Achim · 23. Dezember 2025

  Synthetic Aperture Radar (SAR) offers a unique capability for all-weather, space-based maritime activity monitoring by capturing and imaging strong reflections from ships at sea. A well-defined challenge in this domain is ship type classification. Due to the high diversity and complexity of ship typ…