River Ice Segmentation with Deep Learning
This work addresses the specific problem of river ice monitoring for environmental or hydrological applications, but it is incremental as it applies existing deep learning methods to a new dataset.
The paper tackled the problem of segmenting two types of river ice from digital images to compute surface ice concentration, using state-of-the-art deep learning semantic segmentation methods, and found that these methods could handle challenges like limited labeled data and noisy labels, with results made publicly available for further research.
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.