# River Ice Segmentation with Deep Learning

## TL;DR

This paper explores the application of deep convolutional neural networks for river ice segmentation, addressing challenges like limited labeled data and noisy labels, and evaluates the effectiveness of state-of-the-art methods in this context.

## Contribution

It evaluates the performance of advanced CNN-based semantic segmentation methods on river ice images, highlighting their ability to handle data scarcity and label noise.

## Key findings

- Deep learning methods can partially overcome limited data challenges.
- Noisy labels significantly impact segmentation accuracy.
- Publicly available code and data facilitate further research.

## Abstract

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.