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The Cryosphere An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/tc-2020-231
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-2020-231
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  14 Oct 2020

14 Oct 2020

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This preprint is currently under review for the journal TC.

Calving Front Machine (CALFIN): Glacial Termini Dataset and Automated Deep Learning Extraction Method for Greenland, 1972–2019

Daniel Cheng1, Wayne Hayes1, Eric Larour2, Yara Mohajerani1,3, Michael Wood2, Isabella Velicogna1,2, and Eric Rignot1,2 Daniel Cheng et al.
  • 1University of California at Irvine, Irvine CA, USA
  • 2Jet Propulsion Laboratory, California Institute of Technology, Pasadena CA, USA
  • 3University of Washington, eScience Institute and Department of Civil and Environmental Engineering, Seattle, WA, 98195, USA

Abstract. We present Calving Front Machine (CALFIN), an automated method for extracting calving fronts from satellite images of marine-terminating glaciers. The results use Landsat imagery from 1972 to 2019 to generate 22 678 calving front lines across 66 Greenlandic glaciers. The method uses deep learning, and builds on existing work by Mohajerani et al., Zhang et al., and Baumhoer et al. Additional post-processing techniques allow for accurate segmentation of imagery into Shapefile outputs. This method is uniquely robust to the impact of clouds, illumination differences, ice mélange, and Landsat-7 Scan Line Corrector errors. CALFIN provides improvements on the current state of the art. A model inter-comparison is performed to evaluate performance against existing methodologies. CALFIN's ability to generalize to SAR imagery is also evaluated. CALFIN's fronts are often indistinguishable from manually-curated fronts, deviating by 2.25 pixels (86.76 meters) from the true front on a diverse set of 162 testing images. The current implementation offers a new opportunity to explore sub-seasonal trends on the extent of Greenland's margins, and supplies new constraints for simulations of the evolution of the mass balance of the Greenland Ice Sheet and its contributions to future sea level rise.

Daniel Cheng et al.

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Status: open (until 09 Dec 2020)
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Daniel Cheng et al.

Data sets

CALFIN: Calving Front Dataset for East/West Greenland, 1972-2019 Daniel Cheng, Wayne Hayes, and Eric Larour https://doi.org/10.7280/D1FH5D

Model code and software

CALFIN v1.0.0 Daniel Cheng https://doi.org/10.5281/zenodo.4084403

Daniel Cheng et al.

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Latest update: 25 Oct 2020
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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...
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