<ul><li>Accurate ocean modeling and coastal hazard prediction require high-resolution bathymetric data.</li><li>Existing deep learning methods face difficulties in producing detailed ocean floor maps with consistent structure and quantifiable uncertainties.</li><li>A novel uncertainty-aware mechanism using spatial blocks and block-based conformal prediction is proposed in this work.</li><li>Experimental results show increased reconstruction quality and improved reliability of uncertainty estimation, benefiting climate modeling and coastal hazard assessment.</li></ul>

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

Discover more