Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning ...
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, mos...
| Main Authors: | , , , , , |
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| Format: | Dataset |
| Language: | English |
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Dryad
2020
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.7280/d1vd6g https://datadryad.org/stash/dataset/doi:10.7280/D1VD6G |
| Summary: | 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 ... : 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 ... |
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