  This paper deals with the problem of computing surface ice concentration for
two different types of ice from digital images of river surface. It presents
the results of attempting to solve this problem using several state of the art
semantic segmentation methods based on deep convolutional neural networks
(CNNs). This task presents two main challenges - very limited availability of
labeled training data and presence of noisy labels due to the great difficulty
of visually distinguishing between the two types of ice, even for human
experts. The results are used to analyze the extent to which some of the best
deep learning methods currently in existence can handle these challenges. The
code and data used in the experiments are made publicly available to facilitate
further work in this domain.
