Articles | Volume 14, issue 9
https://doi.org/10.5194/essd-14-4287-2022
© Author(s) 2022. 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-14-4287-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
22 Sep 2022
Data description paper |
| 22 Sep 2022
| 22 Sep 2022
Calving fronts and where to find them: a benchmark dataset and methodology for automatic glacier calving front extraction from synthetic aperture radar imagery
Pattern Recognition Lab, Computer Science Department, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
Thorsten Seehaus
Institute of Geography, Department of Geography and Geosciences, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
Matthias Braun
Institute of Geography, Department of Geography and Geosciences, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
Andreas Maier
Pattern Recognition Lab, Computer Science Department, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
Vincent Christlein
Pattern Recognition Lab, Computer Science Department, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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Cited
16 citations as recorded by crossref.
- AMD-HookNet for Glacier Front Segmentation F. Wu et al. 10.1109/TGRS.2023.3245419
- Capturing the transition from marine to land-terminating glacier from the 126-year retreat history of Nordenskiöldbreen, Svalbard J. Kavan et al. 10.1017/jog.2023.92
- Advances in monitoring glaciological processes in Kalallit Nunaat (Greenland) over the past decades D. Fahrner et al. 10.1371/journal.pclm.0000379
- A Deep Active Contour Model for Delineating Glacier Calving Fronts K. Heidler et al. 10.1109/TGRS.2023.3296539
- 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. 10.5194/essd-17-65-2025
- Calving front monitoring at a subseasonal resolution: a deep learning application for Greenland glaciers E. Loebel et al. 10.5194/tc-18-3315-2024
- Out-of-the-box calving-front detection method using deep learning O. Herrmann et al. 10.5194/tc-17-4957-2023
- A high-resolution calving front data product for marine-terminating glaciers in Svalbard T. Li et al. 10.5194/essd-16-919-2024
- GLA-STDeepLab: SAR Enhancing Glacier and Ice Shelf Front Detection Using Swin-TransDeepLab With Global–Local Attention Q. Zhu et al. 10.1109/TGRS.2023.3324404
- Contextual HookFormer for Glacier Calving Front Segmentation F. Wu et al. 10.1109/TGRS.2024.3368215
- Analysis of continuous calving front retreat and the associated influencing factors of the Thwaites Glacier using high-resolution remote sensing data from 2015 to 2023 Q. Zhu et al. 10.1080/17538947.2024.2390438
- AutoTerm: an automated pipeline for glacier terminus extraction using machine learning and a “big data” repository of Greenland glacier termini E. Zhang et al. 10.5194/tc-17-3485-2023
- Investigating the dynamics and interactions of surface features on Pine Island Glacier using remote sensing and deep learning Q. Zhu et al. 10.1016/j.accre.2024.07.011
- Out-of-the-box calving-front detection method using deep learning O. Herrmann et al. 10.5194/tc-17-4957-2023
- 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. 10.5194/essd-17-65-2025
- Calving fronts and where to find them: a benchmark dataset and methodology for automatic glacier calving front extraction from synthetic aperture radar imagery N. Gourmelon et al. 10.5194/essd-14-4287-2022
13 citations as recorded by crossref.
- AMD-HookNet for Glacier Front Segmentation F. Wu et al. 10.1109/TGRS.2023.3245419
- Capturing the transition from marine to land-terminating glacier from the 126-year retreat history of Nordenskiöldbreen, Svalbard J. Kavan et al. 10.1017/jog.2023.92
- Advances in monitoring glaciological processes in Kalallit Nunaat (Greenland) over the past decades D. Fahrner et al. 10.1371/journal.pclm.0000379
- A Deep Active Contour Model for Delineating Glacier Calving Fronts K. Heidler et al. 10.1109/TGRS.2023.3296539
- 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. 10.5194/essd-17-65-2025
- Calving front monitoring at a subseasonal resolution: a deep learning application for Greenland glaciers E. Loebel et al. 10.5194/tc-18-3315-2024
- Out-of-the-box calving-front detection method using deep learning O. Herrmann et al. 10.5194/tc-17-4957-2023
- A high-resolution calving front data product for marine-terminating glaciers in Svalbard T. Li et al. 10.5194/essd-16-919-2024
- GLA-STDeepLab: SAR Enhancing Glacier and Ice Shelf Front Detection Using Swin-TransDeepLab With Global–Local Attention Q. Zhu et al. 10.1109/TGRS.2023.3324404
- Contextual HookFormer for Glacier Calving Front Segmentation F. Wu et al. 10.1109/TGRS.2024.3368215
- Analysis of continuous calving front retreat and the associated influencing factors of the Thwaites Glacier using high-resolution remote sensing data from 2015 to 2023 Q. Zhu et al. 10.1080/17538947.2024.2390438
- AutoTerm: an automated pipeline for glacier terminus extraction using machine learning and a “big data” repository of Greenland glacier termini E. Zhang et al. 10.5194/tc-17-3485-2023
- Investigating the dynamics and interactions of surface features on Pine Island Glacier using remote sensing and deep learning Q. Zhu et al. 10.1016/j.accre.2024.07.011
3 citations as recorded by crossref.
- Out-of-the-box calving-front detection method using deep learning O. Herrmann et al. 10.5194/tc-17-4957-2023
- 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. 10.5194/essd-17-65-2025
- Calving fronts and where to find them: a benchmark dataset and methodology for automatic glacier calving front extraction from synthetic aperture radar imagery N. Gourmelon et al. 10.5194/essd-14-4287-2022
Latest update: 30 Jan 2025
Short summary
Ice loss of glaciers shows in retreating calving fronts (i.e., the position where icebergs break off the glacier and drift into the ocean). This paper presents a benchmark dataset for calving front delineation in synthetic aperture radar (SAR) images. The dataset can be used to train and test deep learning techniques, which automate the monitoring of the calving front. Provided example models achieve front delineations with an average distance of 887 m to the correct calving front.
Ice loss of glaciers shows in retreating calving fronts (i.e., the position where icebergs break...
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