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Dakota Pyles

4 papers

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papers

Publications (4)

cs.CV2026

Comparison Study: Glacier Calving Front Delineation in Synthetic Aperture Radar Images With Deep Learning

Nora Gourmelon, Konrad Heidler, Erik Loebel +12

Continuous monitoring of glacier calving fronts is essential for sea level rise projections. This study benchmarks Deep Learning systems for front delineation in Synthetic Aperture…

cs.CV2025

SSL4SAR: Self-Supervised Learning for Glacier Calving Front Extraction from SAR Imagery

Nora Gourmelon, Marcel Dreier, Martin Mayr +5

Glaciers are losing ice mass at unprecedented rates, increasing the need for accurate, year-round monitoring to understand frontal ablation, particularly the factors driving the ca…

cs.CV2026

Few-Shot Domain Adaptation with Temporal References and Static Priors for Glacier Calving Front Delineation

Marcel Dreier, Nora Gourmelon, Dakota Pyles +4

During benchmarking, the state-of-the-art model for glacier calving front delineation achieves near-human performance. However, when applied in a real-world setting at a novel stud…

cs.CV2025

Multi-temporal Calving Front Segmentation

Marcel Dreier, Nora Gourmelon, Dakota Pyles +5

The calving fronts of marine-terminating glaciers undergo constant changes. These changes significantly affect the glacier's mass and dynamics, demanding continuous monitoring. To…

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