DOI: 10.7280/d1vd6g
title: Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning

description: Delineating the grounding line of marine-terminating glaciers—where ice
 starts to become afloat in ocean waters—is crucial for measuring and
 understanding ice sheet mass balance, glacier dynamics, and their
 contributions to sea level rise. This task has been previously done using
 time-consuming, mostly-manual digitizations of differential
 interferometric synthetic-aperture radar interferograms by human experts.
 This approach is no longer viable with a fast-growing set of satellite
 observations and the need to establish time series over entire continents
 with quantified uncertainties. We present a fully-convolutional neural
 network with parallel atrous convolutional layers and asymmetric
 encoder/decoder components that automatically delineates grounding lines
 at a large scale, efficiently, and accompanied by uncertainty estimates.
 Our procedure detects grounding lines within 232 m in 100-m
 posting interferograms, which is comparable to the performance achieved by
 human experts. We also find value in the machine learning approach in
 situations that even challenge human experts. We use this approach to map
 the tidal-induced variability in grounding line position around Antarctica
 in 22,935 interferograms from year 2018. Along the Getz Ice Shelf, in West
 Antarctica, we demonstrate that grounding zones are one order magnitude
 (13.3 ± 3.9) wider than expected from hydrostatic
 equilibrium, which justifies the need to map grounding lines repeatedly
 and comprehensively to inform numerical models.
description: The grounding lines for the entire Antarctic coastline for
 available Sentinel1-a/b tracks in 2018 are provided as Shapefiles for the
 6-day and 12-day tracks separately, as "AllTracks_6d_GL.shp" and
 "AllTracks_12d_GL.shp" respectively. The corresponding
 uncertainty estimates are also provided, as described in the manuscript,
 which are labelled as "AllTracks_6d_uncertainty.shp" and
 "AllTracks_12d_uncertainty.shp".  Each
 grounding line in the Shapefile contains 6 attribudes: 
 ID: grounding line ID for each DInSAR
 scene  Type: whether the line was used as training or
 testing data. Class: whether each identifined line is
 a grounding line or a pinning point Length: length of
 the enclosing polygon determining the uncertainty
 Width: width of the enclosing polygon determining the
 uncertainty FILENAME: name of the original shapefile
 for the grounding line (before all files were combined into one), which
 gives all relevant information of the DInSAR data, in the
 format  "gl_[Track#]_[YYMMDD scene1]-[YYMMDD scene2]-[YYMMDD
 scene3]-[YYMMDD scene4]_[Orbit 1]-[Orbit 2]-[Orbit 3]-[Orbit
 4]_T[Acquisition time scene1]_[Acquisition time scene 3]_[noise filter
 length threshold]km.shp". 
 <em>Disclaimer</em>: while these results provide a
 complete mapping of the grounding lines for all of Antarctica from
 available data in 2018, proper interpretation of the results are important
 for scientific analyses in the presence of any noise in the output of the
 neural network. When using this data, please cite the
 accompanying manuscript along with the data. For
 questions, please contact Yara Mohajerani at ymohajer@uci.edu.