  In response to the growing importance of geospatial data, its analysis
including semantic segmentation becomes an increasingly popular task in
computer vision today. Convolutional neural networks are powerful visual models
that yield hierarchies of features and practitioners widely use them to process
remote sensing data. When performing remote sensing image segmentation,
multiple instances of one class with precisely defined boundaries are often the
case, and it is crucial to extract those boundaries accurately. The accuracy of
segments boundaries delineation influences the quality of the whole segmented
areas explicitly. However, widely-used segmentation loss functions such as BCE,
IoU loss or Dice loss do not penalize misalignment of boundaries sufficiently.
In this paper, we propose a novel loss function, namely a differentiable
surrogate of a metric accounting accuracy of boundary detection. We can use the
loss function with any neural network for binary segmentation. We performed
validation of our loss function with various modifications of UNet on a
synthetic dataset, as well as using real-world data (ISPRS Potsdam, INRIA AIL).
Trained with the proposed loss function, models outperform baseline methods in
terms of IoU score.
