Comparison Study: Glacier Calving Front Delineation in Synthetic Aperture Radar Images With Deep Learning.
Where this comes from
- Record sourced from PubMed, PMID 42009335.
- Also identified by DOI 10.1109/TPAMI.2026.3685700.
- No licence information is recorded for this record.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
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.