Bathymetry estimation for coastal regions using self-attention - Xiaoxiong Zhang; Maryam R. Al Shehhi (Scientific Reports)
Abstract 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 $$=1.784$$ m and R2 $$=0.903$$ in the depth down to $$-41$$ m. When testing in challenging unseen coastal regions with different bottom types, our model obtains RMSE $$=3.042$$ m and R2 $$=0.907$$ in the steep slope region with depth down to $$- 28$$ m and obtains RMSE $$=2.577$$ m and R2 $$=0.705$$ in the rugged region with depth down to $$- 16$$ 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 R2 by 0.187 in the steep slope region. The dataset, code, and trained weights of the model are publicly available on GitHub.