CALFIN: Calving front dataset for East/West Greenland, 1972-2019

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.