# River Ice Segmentation with Deep Learning Source: [River Ice Segmentation with Deep Learning]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:04:44.961Z Authors: Abhineet Singh, Hayden Kalke, Mark Loewen, Nilanjan Ray Published: 2019-01-14T17:16:14.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Ice Concentration Estimation on ARIS](): Ice Concentration Estimation; ARIS; Median MAE (%) (Anchor Ice): 4.71. - [Semantic Segmentation on ARIS](): Semantic segmentation; ARIS; Recall (anchor ice): 82.31. ## Original sources and implementation [Original paper]() [PDF]()