  Glacier calving front position (CFP) is an important glaciological variable.
Traditionally, delineating the CFPs has been carried out manually, which was
subjective, tedious and expensive. Automating this process is crucial for
continuously monitoring the evolution and status of glaciers. Recently, deep
learning approaches have been investigated for this application. However, the
current methods get challenged by a severe class-imbalance problem. In this
work, we propose to mitigate the class-imbalance between the calving front
class and the non-calving front class by reformulating the segmentation problem
into a pixel-wise regression task. A Convolutional Neural Network gets
optimized to predict the distance values to the glacier front for each pixel in
the image. The resulting distance map localizes the CFP and is further
post-processed to extract the calving front line. We propose three
post-processing methods, one method based on statistical thresholding, a second
method based on conditional random fields (CRF), and finally the use of a
second U-Net. The experimental results confirm that our approach significantly
outperforms the state-of-the-art methods and produces accurate delineation. The
Second U-Net obtains the best performance results, resulting in an average
improvement of about 21% dice coefficient enhancement.
