Comparison Study: Glacier Calving Front Delineation in Synthetic Aperture Radar Images With Deep Learning
Publikation: Beitrag in Fachzeitschrift › Forschungsartikel › Beigetragen › Begutachtung
Beitragende
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
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 11224-11230 |
| Seitenumfang | 7 |
| Fachzeitschrift | IEEE Transactions on Pattern Analysis and Machine Intelligence |
| Jahrgang | 48 |
| Ausgabenummer | 9 |
| Frühes Online-Datum | 20 Apr. 2026 |
| Publikationsstatus | Veröffentlicht - Sept. 2026 |
| Peer-Review-Status | Ja |
Externe IDs
| ORCID | /0000-0001-9874-9295/work/212490969 |
|---|---|
| Scopus | 105036646329 |
Schlagworte
Schlagwörter
- comparison study, deep learning, foundation model, Glacier calving front delineation, vision transformer