Articles | Volume 15, issue 3
https://doi.org/10.5194/tc-15-1663-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-15-1663-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Apr 2021
Research article |
| 01 Apr 2021
| 01 Apr 2021
Calving Front Machine (CALFIN): glacial termini dataset and automated deep learning extraction method for Greenland, 1972–2019
University of California at Irvine, Irvine, CA, USA
Wayne Hayes
University of California at Irvine, Irvine, CA, USA
Eric Larour
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Yara Mohajerani
University of California at Irvine, Irvine, CA, USA
eScience Institute and Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA
Michael Wood
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Isabella Velicogna
University of California at Irvine, Irvine, CA, USA
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Eric Rignot
University of California at Irvine, Irvine, CA, USA
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
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Cited
37 citations as recorded by crossref.
- Helheim Glacier's Terminus Position Controls Its Seasonal and Inter‐Annual Ice Flow Variability G. Cheng et al. 10.1029/2021GL097085
- Inferring time-dependent calving dynamics at Helheim Glacier J. Downs et al. 10.1017/jog.2022.68
- Mapping Debris-Covered Glaciers Using High-Resolution Imagery (GF-2) and Deep Learning Algorithms X. Yang et al. 10.3390/rs16122062
- Advances in monitoring glaciological processes in Kalallit Nunaat (Greenland) over the past decades D. Fahrner et al. 10.1371/journal.pclm.0000379
- Mapping mountain glaciers using an improved U-Net model with cSE S. Tian et al. 10.1080/17538947.2022.2036834
- Seasonal Tidewater Glacier Terminus Oscillations Bias Multi‐Decadal Projections of Ice Mass Change D. Felikson et al. 10.1029/2021JF006249
- Uncovering Basal Friction in Northwest Greenland Using an Ice Flow Model and Observations of the Past Decade Y. Choi et al. 10.1029/2022JF006710
- Automated glacier extraction using a Transformer based deep learning approach from multi-sensor remote sensing imagery Y. Peng et al. 10.1016/j.isprsjprs.2023.06.015
- Out-of-the-box calving-front detection method using deep learning O. Herrmann et al. 10.5194/tc-17-4957-2023
- Deep learning speeds up ice flow modelling by several orders of magnitude G. Jouvet et al. 10.1017/jog.2021.120
- 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
- TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applications S. Goliber et al. 10.5194/tc-16-3215-2022
- 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
- On Mathews Correlation Coefficient and Improved Distance Map Loss for Automatic Glacier Calving Front Segmentation in SAR Imagery A. Davari et al. 10.1109/TGRS.2021.3115883
- Intelligent energy and ecosystem for real-time monitoring of glaciers S. Kimothi et al. 10.1016/j.compeleceng.2022.108163
- A second-order attention network for glacial lake segmentation from remotely sensed imagery S. Wang et al. 10.1016/j.isprsjprs.2022.05.007
- Pixelwise Distance Regression for Glacier Calving Front Detection and Segmentation A. Davari et al. 10.1109/TGRS.2022.3158591
- 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
- Seasonal Patterns of Greenland Ice Velocity From Sentinel‐1 SAR Data Linked to Runoff A. Solgaard et al. 10.1029/2022GL100343
- Seasonal Flow Types of Glaciers in Sermilik Fjord, Greenland, Over 2016–2021 K. Poinar 10.1029/2022JF006901
- IceLines – A new data set of Antarctic ice shelf front positions C. Baumhoer et al. 10.1038/s41597-023-02045-x
- Image classification of marine-terminating outlet glaciers in Greenland using deep learning methods M. Marochov et al. 10.5194/tc-15-5041-2021
- Brief communication: Preliminary ICESat-2 (Ice, Cloud and land Elevation Satellite-2) measurements of outlet glaciers reveal heterogeneous patterns of seasonal dynamic thickness change C. Taubenberger et al. 10.5194/tc-16-1341-2022
- AMD-HookNet for Glacier Front Segmentation F. Wu et al. 10.1109/TGRS.2023.3245419
- Ubiquitous acceleration in Greenland Ice Sheet calving from 1985 to 2022 C. Greene et al. 10.1038/s41586-023-06863-2
- Extracting Glacier Calving Fronts by Deep Learning: The Benefit of Multispectral, Topographic, and Textural Input Features E. Loebel et al. 10.1109/TGRS.2022.3208454
- A high-resolution calving front data product for marine-terminating glaciers in Svalbard T. Li et al. 10.5194/essd-16-919-2024
- Tidally Modulated Glacial Slip and Tremor at Helheim Glacier, Greenland P. Yan et al. 10.1029/2023GL105342
- Simulating surface height and terminus position for marine outlet glaciers using a level set method with data assimilation M. Hossain et al. 10.1016/j.jcp.2022.111766
- A principled representation of elongated structures using heatmaps F. Kordon et al. 10.1038/s41598-023-41221-2
- Automatic calving front extraction from digital elevation model-derived data Y. Dong et al. 10.1016/j.rse.2021.112854
- A Deep Active Contour Model for Delineating Glacier Calving Fronts K. Heidler et al. 10.1109/TGRS.2023.3296539
- Glacier extraction based on high-spatial-resolution remote-sensing images using a deep-learning approach with attention mechanism X. Chu et al. 10.5194/tc-16-4273-2022
- Pan-Greenland mapping of supraglacial rivers, lakes, and water-filled crevasses in a cool summer (2018) and a warm summer (2019) W. Zhang et al. 10.1016/j.rse.2023.113781
- Updating glacier inventories on the periphery of Antarctica and Greenland using multi-source data X. Liu et al. 10.1017/aog.2023.75
- How to Get the Most Out of U-Net for Glacier Calving Front Segmentation M. Periyasamy et al. 10.1109/JSTARS.2022.3148033
37 citations as recorded by crossref.
- Helheim Glacier's Terminus Position Controls Its Seasonal and Inter‐Annual Ice Flow Variability G. Cheng et al. 10.1029/2021GL097085
- Inferring time-dependent calving dynamics at Helheim Glacier J. Downs et al. 10.1017/jog.2022.68
- Mapping Debris-Covered Glaciers Using High-Resolution Imagery (GF-2) and Deep Learning Algorithms X. Yang et al. 10.3390/rs16122062
- Advances in monitoring glaciological processes in Kalallit Nunaat (Greenland) over the past decades D. Fahrner et al. 10.1371/journal.pclm.0000379
- Mapping mountain glaciers using an improved U-Net model with cSE S. Tian et al. 10.1080/17538947.2022.2036834
- Seasonal Tidewater Glacier Terminus Oscillations Bias Multi‐Decadal Projections of Ice Mass Change D. Felikson et al. 10.1029/2021JF006249
- Uncovering Basal Friction in Northwest Greenland Using an Ice Flow Model and Observations of the Past Decade Y. Choi et al. 10.1029/2022JF006710
- Automated glacier extraction using a Transformer based deep learning approach from multi-sensor remote sensing imagery Y. Peng et al. 10.1016/j.isprsjprs.2023.06.015
- Out-of-the-box calving-front detection method using deep learning O. Herrmann et al. 10.5194/tc-17-4957-2023
- Deep learning speeds up ice flow modelling by several orders of magnitude G. Jouvet et al. 10.1017/jog.2021.120
- 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
- TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applications S. Goliber et al. 10.5194/tc-16-3215-2022
- 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
- On Mathews Correlation Coefficient and Improved Distance Map Loss for Automatic Glacier Calving Front Segmentation in SAR Imagery A. Davari et al. 10.1109/TGRS.2021.3115883
- Intelligent energy and ecosystem for real-time monitoring of glaciers S. Kimothi et al. 10.1016/j.compeleceng.2022.108163
- A second-order attention network for glacial lake segmentation from remotely sensed imagery S. Wang et al. 10.1016/j.isprsjprs.2022.05.007
- Pixelwise Distance Regression for Glacier Calving Front Detection and Segmentation A. Davari et al. 10.1109/TGRS.2022.3158591
- 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
- Seasonal Patterns of Greenland Ice Velocity From Sentinel‐1 SAR Data Linked to Runoff A. Solgaard et al. 10.1029/2022GL100343
- Seasonal Flow Types of Glaciers in Sermilik Fjord, Greenland, Over 2016–2021 K. Poinar 10.1029/2022JF006901
- IceLines – A new data set of Antarctic ice shelf front positions C. Baumhoer et al. 10.1038/s41597-023-02045-x
- Image classification of marine-terminating outlet glaciers in Greenland using deep learning methods M. Marochov et al. 10.5194/tc-15-5041-2021
- Brief communication: Preliminary ICESat-2 (Ice, Cloud and land Elevation Satellite-2) measurements of outlet glaciers reveal heterogeneous patterns of seasonal dynamic thickness change C. Taubenberger et al. 10.5194/tc-16-1341-2022
- AMD-HookNet for Glacier Front Segmentation F. Wu et al. 10.1109/TGRS.2023.3245419
- Ubiquitous acceleration in Greenland Ice Sheet calving from 1985 to 2022 C. Greene et al. 10.1038/s41586-023-06863-2
- Extracting Glacier Calving Fronts by Deep Learning: The Benefit of Multispectral, Topographic, and Textural Input Features E. Loebel et al. 10.1109/TGRS.2022.3208454
- A high-resolution calving front data product for marine-terminating glaciers in Svalbard T. Li et al. 10.5194/essd-16-919-2024
- Tidally Modulated Glacial Slip and Tremor at Helheim Glacier, Greenland P. Yan et al. 10.1029/2023GL105342
- Simulating surface height and terminus position for marine outlet glaciers using a level set method with data assimilation M. Hossain et al. 10.1016/j.jcp.2022.111766
- A principled representation of elongated structures using heatmaps F. Kordon et al. 10.1038/s41598-023-41221-2
- Automatic calving front extraction from digital elevation model-derived data Y. Dong et al. 10.1016/j.rse.2021.112854
- A Deep Active Contour Model for Delineating Glacier Calving Fronts K. Heidler et al. 10.1109/TGRS.2023.3296539
- Glacier extraction based on high-spatial-resolution remote-sensing images using a deep-learning approach with attention mechanism X. Chu et al. 10.5194/tc-16-4273-2022
- Pan-Greenland mapping of supraglacial rivers, lakes, and water-filled crevasses in a cool summer (2018) and a warm summer (2019) W. Zhang et al. 10.1016/j.rse.2023.113781
- Updating glacier inventories on the periphery of Antarctica and Greenland using multi-source data X. Liu et al. 10.1017/aog.2023.75
- How to Get the Most Out of U-Net for Glacier Calving Front Segmentation M. Periyasamy et al. 10.1109/JSTARS.2022.3148033
Latest update: 29 Jun 2024
Short summary
Tracking changes in Greenland's glaciers is important for understanding Earth's climate, but it is time consuming to do so by hand. We train a program, called CALFIN, to automatically track these changes with human levels of accuracy. CALFIN is a special type of program called a neural network. This method can be applied to other glaciers and eventually other tracking tasks. This will enhance our understanding of the Greenland Ice Sheet and permit better models of Earth's climate.
Tracking changes in Greenland's glaciers is important for understanding Earth's climate, but it...
