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Choosing how to encode a real-world problem as a machine learning task is animportant design decision in machine learning. The task of glacier calvingfront modeling has often been approached as a semantic segmentation task.Recent studies have shown that combining segmentation with edge detection canimprove the accuracy of calving front detectors. Building on this observation,we completely rephrase the task as a contour tracing problem and propose amodel for explicit contour detection that does not incorporate any densepredictions as intermediate steps. The proposed approach, called ``ChartingOutlines by Recurrent Adaptation'' (COBRA), combines Convolutional NeuralNetworks (CNNs) for feature extraction and active contour models for thedelineation. By training and evaluating on several large-scale datasets ofGreenland's outlet glaciers, we show that this approach indeed outperforms theaforementioned methods based on segmentation and edge-detection. Finally, wedemonstrate that explicit contour detection has benefits over pixel-wisemethods when quantifying the models' prediction uncertainties. The project pagecontaining the code and animated model predictions can be found at\url{https://khdlr.github.io/COBRA/}.

A Deep Active Contour Model for Delineating Glacier Calving Fronts