EGU2020-19979
https://doi.org/10.5194/egusphere-egu2020-19979
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Calving Front Machine (CALFIN): Automated Calving Front Dataset and Deep Learning Methodology for East/West Greenland, 1972-2019

Daniel Cheng1, Yara Mohajerani1, Michael Wood2, Eric Larour2, Wayne Hayes1, Isabella Velicogna1, and Eric Rignot1,2
Daniel Cheng et al.
  • 1University of California, Irvine, Westminster, United States of America (dlcheng@uci.edu)
  • 2Jet Propulsion Laboratory, California Institute of Technology, Pasadena CA, USA

We present Calving Front Machine (CALFIN), an automated method for extracting calving fronts from satellite imagery. We generate results for 66 glaciers along East/West Greenland from 1972 to 2019. We output these results as a dataset, and provide new constraints on glacial evolution over the time period. This method is uniquely robust to clouds, illumination differences, ice mélange, and Landsat-7 Scan Line Corrector errors. The current implementation offers a new opportunity to explore previous trends, and validate existing models moving forward.

This method utilizes deep learning, in the form of the Google DeeplabV3+ Xception derived CALFIN Neural Network. This approach builds on existing work by Mohajerani et al., Zhang et al., and Baumhoer et al. Additional post-processing techniques allow our method to achieve accurate and useful segmentation of raw images into Shapefile outputs. 

We achieve are often indistinguishable from the manually-curated fronts, deviating from such test data by 1 pixel (about 80 meters) or less XXX% of the time across 162 test images.

CALFIN excels among the current state of the art. We show this by performing a model inter-comparison to evaluate CALFIN's performance against existing methodologies. We also showcase CALFIN's ability to generalize to SAR and MODIS imagery. We achieve a mean error of 2.25 pixels (86.76 meters) from the true front on a diverse set of 162 testing images.

How to cite: Cheng, D., Mohajerani, Y., Wood, M., Larour, E., Hayes, W., Velicogna, I., and Rignot, E.: Calving Front Machine (CALFIN): Automated Calving Front Dataset and Deep Learning Methodology for East/West Greenland, 1972-2019, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19979, https://doi.org/10.5194/egusphere-egu2020-19979, 2020

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