Accurate, detailed, and regularly updated bathymetry, coupled with complex semantic content, is essential for under-mapped shallow-water environments facing increasing climatological and anthropogenic pressures. However, existing approaches that derive either depth or seabed classes from remote sensing imagery treat these tasks in isolation, forfeiting the mutual benefits of their interaction and hindering the broader adoption of deep learning methods. To address these limitations, we introduce Seabed-Net, a unified multi-task framework that simultaneously predicts bathymetry and pixel-based seabed classification from remote sensing imagery of various resolutions. Seabed-Net employs dual-branch encoders for bathymetry estimation and pixel-based seabed classification, integrates cross-task features via an Attention Feature Fusion module and a windowed Swin-Transformer fusion block, and balances objectives through dynamic task uncertainty weighting. In extensive evaluations at two heterogeneous coastal sites, it consistently outperforms traditional empirical models and traditional machine learning regression methods, achieving up to 75\% lower RMSE. It also reduces bathymetric RMSE by 10-30\% compared to state-of-the-art single-task and multi-task baselines and improves seabed classification accuracy up to 8\%. Qualitative analyses further demonstrate enhanced spatial consistency, sharper habitat boundaries, and corrected depth biases in low-contrast regions. These results confirm that jointly modeling depth with both substrate and seabed habitats yields synergistic gains, offering a robust, open solution for integrated shallow-water mapping. Code and pretrained weights are available at https://github.com/pagraf/Seabed-Net.

翻译：精确、详细且定期更新的水深数据，结合复杂的语义信息，对于面临日益增长的气候和人为压力、测绘不足的浅水环境至关重要。然而，现有从遥感影像中获取水深或海底类别的方法均孤立地处理这些任务，放弃了二者交互的互惠优势，并阻碍了深度学习方法的更广泛采用。为解决这些局限性，我们提出了Seabed-Net，一个统一的多任务框架，能够从不同分辨率的遥感影像中同时预测水深和基于像素的海底分类。Seabed-Net采用双分支编码器分别用于水深估算和基于像素的海底分类，通过注意力特征融合模块和窗口化Swin-Transformer融合块整合跨任务特征，并利用动态任务不确定性加权来平衡目标。在两个异质性海岸站点的广泛评估中，该模型始终优于传统经验模型和传统机器学习回归方法，实现了高达75%的均方根误差降低。与最先进的单任务和多任务基线相比，它将水深均方根误差降低了10-30%，并将海底分类精度提高了高达8%。定性分析进一步证明了其在空间一致性增强、栖息地边界更清晰以及低对比度区域深度偏差校正方面的优势。这些结果证实，将深度与底质和海底栖息地联合建模能产生协同增益，为集成式浅水测绘提供了一个稳健、开放的解决方案。代码和预训练权重可在 https://github.com/pagraf/Seabed-Net 获取。