Reconstructing Shallow River Bathymetry Through Sequence-Based Modeling Approach

Hydrological monitoring is crucial for protecting aquatic ecosystems, especially downstream of hydropower plants where water levels can change suddenly and cause the degradation of instream habitats. There are lot of traditional methods used to monitor water levels and river bathymetry, but most of them rely on in situ measurements. Drone-based remote sensing has received more attention in recent years, with the data in turn processed using CNNs. In this paper, we propose a new sequence-based method that uses multiple frames to expand the available context and compare it to already existing methods, such as Lyzenga, Stumpf, CNN, and SfM. The best performing models within this study end up being SfM and CNN, with the former being more accurate on rivers with clean riverbeds and the latter being the most consistent. The sequence-based model shows promise, and even outperforms CNN, in terms of MAE, on rivers where the same location across multiple views is mapped, achieving the most accurate results across different images. This shows that utilizing multiple views to increase the available context can improve the accuracy of riverine depth estimation based on multispectral visual information.