Learning a River Network Extractor Using an Adaptive Loss Function
We have created a deep-learning-based river network extraction model, called DeepRiver, that learns the characteristics of rivers from synthetic data and generalizes them to natural data. To train this model, we created a very large database of exemplary synthetic local channel segments, including channel intersections. Our model uses a special loss function that automatically shifts the focus to the hardest-to-learn parts of an input image. This adaptive loss function makes it possible to learn to detect river centerlines, including the centerlines at junctions and bifurcations. DeepRiver learns to separate between rivers and oceans, and therefore, it is able to reliably extract rivers in coastal regions. The model produces maps of river centerlines, which have the potential to be quite useful for analyzing the properties of river networks.