Learning Enhanced Structural Representations with Block-Based Uncertainties for Ocean Floor Mapping

ArXi:2504.14372v1 Announce Type: cross Accurate ocean modeling and coastal hazard prediction depend on high-resolution bathymetric data; yet, current worldwide datasets are too coarse for exact numerical simulations. While recent deep learning advances have improved earth observation data resolution, existing methods struggle with the unique challenges of producing detailed ocean floor maps, especially in maintaining physical structure consistency and quantifying uncertainties.