mlsanityData to 7 Oct 2026

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

arXiv 2501.05281 ↗First posted 9 Jan 2025cs.CV cs.LGcited by 2

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Abstract (arXiv)

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 Radar imagery. While Deep Learning systems exhibit errors up to 221 m, human annotators deviate by only 38 m, underscoring the need for further research.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 27 of its 88 references that are in the map. First posted on arXiv 9 Jan 2025.

Builds on

27 of 88 references.

References · 27

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15 more
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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025SSL4SAR: Self-Supervised Learning for Glacier Calving Front Extraction from SAR Imagerycited by 1
  2. 2025AMD-HookNet++: Evolution of AMD-HookNet with Hybrid CNN-Transformer Feature Enhancement for Glacier Calving Front Segmentationcited by 0