Zhang Xiaoxiong, Al Shehhi Maryam R
Department of Civil and Environmental Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE.
Sci Rep. 2025 Jan 6;15(1):970. doi: 10.1038/s41598-024-83705-9.
Bathymetric mapping of the coastal area is essential for coastal development and management. However, conventional bathymetric measurement in coastal areas is resource-expensive and under many constraints. Various research have been conducted to improve the efficiency or effectiveness of bathymetric estimations. Among them, Satellite-Derived Bathymetry (SDB) shows the greatest promise in providing a cost-effective and efficient solution due to the spatial and temporal resolution offered by satellite imagery. However, the majority of the SDB models are designed for regional bathymetry, which requires prior knowledge of the tested region. This strongly constrains their application to other regions. In this work, we present TransBathy, a deep-learning-based satellite-derived bathymetric model, to solve the coastal bathymetric mapping for different unknown challenging terrains. This model is purposefully crafted to simultaneously assimilate deep and spatial features by employing an attention mechanism. In addition, we collected a large-scale bathymetric dataset covering different shallow coastal regions across the world, including Honolulu Island, Abu Dhabi, Puerto Rico, etc. We trained the model using the collected dataset in an end-to-end manner. We validated the robustness and effectiveness of our model by conducting extensive experiments, including pre-seen and un-seen regions bathymetric estimations. When testing on pre-seen coastal regions in different locations of the world, our model achieves a good performance with an RMSE [Formula: see text] m and R [Formula: see text] in the depth down to [Formula: see text] m. When testing in challenging unseen coastal regions with different bottom types, our model obtains RMSE [Formula: see text] m and R [Formula: see text] in the steep slope region with depth down to [Formula: see text] m and obtains RMSE [Formula: see text] m and R [Formula: see text] in the rugged region with depth down to [Formula: see text] m. Our model even surpasses the baseline SDB method that is pre-trained in these regions by substantially reducing the RMSE by 0.978m and improving the R by 0.187 in the steep slope region. The dataset, code, and trained weights of the model are publicly available on GitHub.
沿海地区的水深测量绘图对于沿海开发和管理至关重要。然而，沿海地区传统的水深测量成本高昂且受到诸多限制。人们已经开展了各种研究来提高水深估计的效率或效果。其中，卫星衍生测深法（SDB）由于卫星图像提供的空间和时间分辨率，在提供具有成本效益和高效的解决方案方面显示出最大的潜力。然而，大多数SDB模型是为区域测深设计的，这需要对测试区域有先验知识。这严重限制了它们在其他区域的应用。在这项工作中，我们提出了TransBathy，一种基于深度学习的卫星衍生测深模型，以解决不同未知具有挑战性地形的沿海测深绘图问题。该模型通过采用注意力机制，有针对性地精心设计以同时吸收深度和空间特征。此外，我们收集了一个大规模的水深数据集，覆盖了世界各地不同的浅海沿岸地区，包括檀香山岛、阿布扎比、波多黎各等。我们使用收集到的数据集以端到端的方式训练该模型。我们通过进行广泛的实验，包括对已见和未见区域的水深估计，验证了我们模型的稳健性和有效性。当在世界不同地点的已见沿海区域进行测试时，我们的模型在深度达[公式：见原文]米时，均方根误差（RMSE）为[公式：见原文]米，相关系数（R）为[公式：见原文]，表现良好。当在具有不同海底类型的具有挑战性的未见沿海区域进行测试时，我们的模型在深度达[公式：见原文]米的陡坡区域获得的RMSE为[公式：见原文]米，R为[公式：见原文]，在深度达[公式：见原文]米的崎岖区域获得的RMSE为[公式：见原文]米，R为[公式：见原文]。我们的模型甚至超越了在这些区域进行预训练的基线SDB方法，在陡坡区域将RMSE大幅降低了0.978米，将R提高了0.187。该模型的数据集、代码和训练权重可在GitHub上公开获取。
