Articles | Volume 16, issue 2
https://doi.org/10.5194/essd-16-919-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-919-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A high-resolution calving front data product for marine-terminating glaciers in Svalbard
Bristol Glaciology Centre, School of Geographical Sciences, University of Bristol, Bristol BS8 1SS, UK
Chair of Data Science in Earth Observation, Department of Aerospace and Geodesy, Technical University of Munich, Munich 80333, Germany
Konrad Heidler
Chair of Data Science in Earth Observation, Department of Aerospace and Geodesy, Technical University of Munich, Munich 80333, Germany
Lichao Mou
Chair of Data Science in Earth Observation, Department of Aerospace and Geodesy, Technical University of Munich, Munich 80333, Germany
Ádám Ignéczi
Bristol Glaciology Centre, School of Geographical Sciences, University of Bristol, Bristol BS8 1SS, UK
Xiao Xiang Zhu
Chair of Data Science in Earth Observation, Department of Aerospace and Geodesy, Technical University of Munich, Munich 80333, Germany
Munich Center for Machine Learning, Technical University of Munich, Munich 80333, Germany
Jonathan L. Bamber
Bristol Glaciology Centre, School of Geographical Sciences, University of Bristol, Bristol BS8 1SS, UK
Chair of Data Science in Earth Observation, Department of Aerospace and Geodesy, Technical University of Munich, Munich 80333, Germany
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Cited
13 citations as recorded by crossref.
- A decade of monthly frontal ablation at 147 tidewater glaciers in Svalbard D. Pyles et al. https://doi.org/10.5194/essd-18-7319-2026
- 2024 ESA-ECMWF workshop report: current status, progress and opportunities in machine learning for Earth system observation and prediction P. Ebel et al. https://doi.org/10.1038/s41612-024-00757-4
- Svalbard glacier calving front changes from the 1930s to the 1970s from archived satellite and aerial images L. Danjou et al. https://doi.org/10.5194/tc-20-4133-2026
- Distinct characteristics and mechanisms of two tidewater glaciers exhibiting velocity anomalies in western Svalbard W. Su et al. https://doi.org/10.1080/15481603.2025.2596939
- Pervasive glacier retreats across Svalbard from 1985 to 2023 T. Li et al. https://doi.org/10.1038/s41467-025-55948-1
- Acoustic activity indicates submarine melt at tidewater glaciers H. Vishnu et al. https://doi.org/10.1017/jog.2025.10061
- A decade of summer glacier fronts and calved glacier ice distribution in Kongs- and Krossfjorden, Svalbard, observed by Sentinel-1 extra wide swath J. Haarpaintner et al. https://doi.org/10.1016/j.rse.2026.115693
- Calving front positions for 42 key glaciers of the Antarctic Peninsula Ice Sheet: a sub-seasonal record from 2013 to 2023 based on deep-learning application to Landsat multi-spectral imagery E. Loebel et al. https://doi.org/10.5194/essd-17-65-2025
- CISNet: Change information guided semantic segmentation network for automatic extraction of glacier calving fronts J. Zhao et al. https://doi.org/10.1016/j.isprsjprs.2025.08.001
- Multifractal analysis of the perimeters of glaciers in the Svalbard Archipelago M. Carpineti et al. https://doi.org/10.1088/2632-072X/ad5263
- Billefjorden’s benthic biodiversity: the impact of glacier retreat on faunal communities in a high Arctic fjord A. van der Kamp et al. https://doi.org/10.1007/s00300-025-03390-w
- A Scoping Review of Automated Calving Front Detection in Satellite Images and Calving Front Position Datasets W. Milczarek et al. https://doi.org/10.3390/rs18070969
- Surging glaciers in Svalbard: Observing their distribution, characteristics and evolution W. Harcourt et al. https://doi.org/10.1016/j.earscirev.2026.105410
13 citations as recorded by crossref.
- A decade of monthly frontal ablation at 147 tidewater glaciers in Svalbard D. Pyles et al. https://doi.org/10.5194/essd-18-7319-2026
- 2024 ESA-ECMWF workshop report: current status, progress and opportunities in machine learning for Earth system observation and prediction P. Ebel et al. https://doi.org/10.1038/s41612-024-00757-4
- Svalbard glacier calving front changes from the 1930s to the 1970s from archived satellite and aerial images L. Danjou et al. https://doi.org/10.5194/tc-20-4133-2026
- Distinct characteristics and mechanisms of two tidewater glaciers exhibiting velocity anomalies in western Svalbard W. Su et al. https://doi.org/10.1080/15481603.2025.2596939
- Pervasive glacier retreats across Svalbard from 1985 to 2023 T. Li et al. https://doi.org/10.1038/s41467-025-55948-1
- Acoustic activity indicates submarine melt at tidewater glaciers H. Vishnu et al. https://doi.org/10.1017/jog.2025.10061
- A decade of summer glacier fronts and calved glacier ice distribution in Kongs- and Krossfjorden, Svalbard, observed by Sentinel-1 extra wide swath J. Haarpaintner et al. https://doi.org/10.1016/j.rse.2026.115693
- Calving front positions for 42 key glaciers of the Antarctic Peninsula Ice Sheet: a sub-seasonal record from 2013 to 2023 based on deep-learning application to Landsat multi-spectral imagery E. Loebel et al. https://doi.org/10.5194/essd-17-65-2025
- CISNet: Change information guided semantic segmentation network for automatic extraction of glacier calving fronts J. Zhao et al. https://doi.org/10.1016/j.isprsjprs.2025.08.001
- Multifractal analysis of the perimeters of glaciers in the Svalbard Archipelago M. Carpineti et al. https://doi.org/10.1088/2632-072X/ad5263
- Billefjorden’s benthic biodiversity: the impact of glacier retreat on faunal communities in a high Arctic fjord A. van der Kamp et al. https://doi.org/10.1007/s00300-025-03390-w
- A Scoping Review of Automated Calving Front Detection in Satellite Images and Calving Front Position Datasets W. Milczarek et al. https://doi.org/10.3390/rs18070969
- Surging glaciers in Svalbard: Observing their distribution, characteristics and evolution W. Harcourt et al. https://doi.org/10.1016/j.earscirev.2026.105410
Saved (final revised paper)
Latest update: 23 Oct 2026
Short summary
Our study uses deep learning to produce a new high-resolution calving front dataset for 149 marine-terminating glaciers in Svalbard from 1985 to 2023, containing 124 919 terminus traces. This dataset offers insights into understanding calving mechanisms and can help improve glacier frontal ablation estimates as a component of the integrated mass balance assessment.
Our study uses deep learning to produce a new high-resolution calving front dataset for 149...
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