  The continuous monitoring of a shore plays an essential role in designing
strategies for shore protection against erosion. To avoid the effect of clouds
and sunlight, satellite-based imagery with synthetic aperture radar is used to
provide the required data. We show how such data can be processed using
state-of-the-art methods, namely, by a deep-learning-based approach, to detect
the coastline location. We split the process into data reading, data
preprocessing, model training, inference, ensembling, and postprocessing, and
describe the best techniques for each of the parts. Finally, we present our own
solution that is able to precisely extract the coastline from an image even if
it is not recognizable by a human. Our solution has been validated against the
real GPS location of the coastline during Signate's competition, where it was
runner-up among 109 teams across the whole world.
