Coastline extraction from ALOS-2 satellite SAR images

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. In contrast to standard model-driven methods, we present a deep-learning-based approach to detect coastlines in such data. We split the process into data preprocessing, model training, inference, ensembling, and postprocessing and describe the best techniques for each of the parts. To deal with a small training dataset, we propose a novel multi-sample mosaicing augmentation that helps the deep neural network models to reduce overfitting during training. Our solution has been validated against the real Global Positioning System location of coastlines during a worldwide competition organized by Signate and Japan Aerospace Exploration Agency, where it was runner-up among 109 teams from the whole world.