A Deep Active Contour Model for Delineating Glacier Calving Fronts
This work addresses glacier monitoring for climate science, offering a more accurate method for delineating calving fronts, though it is incremental as it builds on prior segmentation and edge-detection approaches.
The paper tackles glacier calving front delineation by reframing it as a contour tracing problem, proposing COBRA, which combines CNNs and active contour models, and shows it outperforms segmentation-based methods on datasets of Greenland's outlet glaciers.
Choosing how to encode a real-world problem as a machine learning task is an important design decision in machine learning. The task of glacier calving front modeling has often been approached as a semantic segmentation task. Recent studies have shown that combining segmentation with edge detection can improve the accuracy of calving front detectors. Building on this observation, we completely rephrase the task as a contour tracing problem and propose a model for explicit contour detection that does not incorporate any dense predictions as intermediate steps. The proposed approach, called ``Charting Outlines by Recurrent Adaptation'' (COBRA), combines Convolutional Neural Networks (CNNs) for feature extraction and active contour models for the delineation. By training and evaluating on several large-scale datasets of Greenland's outlet glaciers, we show that this approach indeed outperforms the aforementioned methods based on segmentation and edge-detection. Finally, we demonstrate that explicit contour detection has benefits over pixel-wise methods when quantifying the models' prediction uncertainties. The project page containing the code and animated model predictions can be found at \url{https://khdlr.github.io/COBRA/}.