Abstract
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. This work presents a novel uncertainty-aware mechanism using spatial blocks to efficiently capture local bathymetric complexity based on block-based conformal prediction. Using the Vector Quantized Variational Autoencoder (VQ-VAE) architecture, the integration of this uncertainty quantification framework yields spatially adaptive confidence estimates while preserving topographical features via discrete latent representations. With smaller uncertainty widths in well-characterized areas and appropriately larger bounds in areas of complex seafloor structures, the block-based design adapts uncertainty estimates to local bathymetric complexity. Compared to conventional techniques, experimental results over several ocean regions show notable increases in both reconstruction quality and uncertainty estimation reliability. This framework increases the reliability of bathymetric reconstructions by preserving structural integrity while offering spatially adaptive uncertainty estimates, so opening the path for more solid climate modeling and coastal hazard assessment.
Abstract (translated)
精确的海洋建模和海岸灾害预测依赖于高分辨率的海底地形数据；然而，目前全球范围内的数据集对于进行准确数值模拟来说过于粗糙。尽管最近深度学习的进步提高了地球观测数据的分辨率，但现有的方法在生成详细海底地图时仍然面临挑战，尤其是在保持物理结构一致性和量化不确定性方面。这项工作提出了一种使用基于空间块的共形预测来有效捕捉局部海底复杂性的不确定度感知机制。通过整合向量量化变分自编码器（VQ-VAE）架构，该不确定性量化框架能够生成适应性地反映地理特征的空间置信估计，并且利用离散潜在表示法保持地形特征。在结构特征明确的区域中，该框架产生的不确定性宽度较小，在海底构造复杂的区域则适当扩大了范围，从而根据当地的海底复杂度调整不确定性估算值。 与传统技术相比，实验结果表明，在多个海洋区域的应用中，该方法不仅显著提高了重建质量，而且增强了不确定性估计的可靠性。这种框架通过保持结构完整性并提供空间自适应性的不确定性估测，增加了海底地形重建的可信度，并为更加可靠的气候建模和海岸灾害评估开辟了新的途径。
URL
https://arxiv.org/abs/2504.14372
