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As the U.S. Army prepares for future conflicts and multi-domain operations, the need for methods to rapidly and continuously characterize the land-sea interface during littoral entry is paramount to ensure maneuverability across these domains. In the maritime domain, nearshore bathymetry and surf-zone sandbars define water depth and wave behavior, which in-turn drive landing tactics and the feasibility and configuration of littoral operations. In the land domain, beach and dune topography define slopes and transit paths, which drive staging area locations and effect maneuverability of both troops and equipment. Accurately predicting surf-zone state and littoral morphology evolution requires synthesizing a range of complex non-linear physics that drive these changes. Using imagery of the littorals from unmanned aerial systems and physics-based models, the U.S. Army Engineer and Development Center has developed novel data assimilation approaches to estimate water depth, littoral conditions, and beach sub-aerial topography from wave kinematics and photogrammetric algorithms and quantify their corresponding uncertainties. To improve the usefulness (speed of the calculations) and accuracy (accounting for known errors related to optical transfer functions and nonlinear wave dynamics) of this technology during littoral operations, approaches to develop machine-learning based computational tools which can directly translate short-sequences of littoral imagery into surf-zone characterization in real time by substituting or augmenting computationally complex models are being investigated. To accomplish this, a photo-realistic, non-linear wave model, Celeris, is used to generate synthetic imagery of a range of surf-zone environments. This synthetic imagery is crucial to developing the data sets necessary to train deep neural networks to solve the non-linear depth inversion problem from observations of wave kinematics.
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