  Deep learning-based coastline detection algorithms have begun to outshine
traditional statistical methods in recent years. However, they are usually
trained only as single-purpose models to either segment land and water or
delineate the coastline. In contrast to this, a human annotator will usually
keep a mental map of both segmentation and delineation when performing manual
coastline detection. To take into account this task duality, we therefore
devise a new model to unite these two approaches in a deep learning model. By
taking inspiration from the main building blocks of a semantic segmentation
framework (UNet) and an edge detection framework (HED), both tasks are combined
in a natural way. Training is made efficient by employing deep supervision on
side predictions at multiple resolutions. Finally, a hierarchical attention
mechanism is introduced to adaptively merge these multiscale predictions into
the final model output. The advantages of this approach over other traditional
and deep learning-based methods for coastline detection are demonstrated on a
dataset of Sentinel-1 imagery covering parts of the Antarctic coast, where
coastline detection is notoriously difficult. An implementation of our method
is available at \url{https://github.com/khdlr/HED-UNet}.
