Comparison Study: Glacier Calving Front Delineation in Synthetic Aperture Radar Images With Deep Learning
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
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.
Details
| Original language | English |
|---|---|
| Pages (from-to) | 11224-11230 |
| Number of pages | 7 |
| Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
| Volume | 48 |
| Issue number | 9 |
| Early online date | 20 Apr 2026 |
| Publication status | Published - Sept 2026 |
| Peer-reviewed | Yes |
External IDs
| ORCID | /0000-0001-9874-9295/work/212490969 |
|---|---|
| Scopus | 105036646329 |
Keywords
Keywords
- comparison study, deep learning, foundation model, Glacier calving front delineation, vision transformer