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

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.