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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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...
