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...

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Bibliographic Details
Main Authors: Mohajerani, Yara, Jeong, Seongsu, Scheuchl, Bernd, Velicogna, Isabella, Rignot, Eric, Milillo, Pietro
Format: Dataset
Language:English
Published: Dryad 2020
Subjects:
Online Access:https://dx.doi.org/10.7280/d1vd6g
https://datadryad.org/stash/dataset/doi:10.7280/D1VD6G
id ftdatacite:10.7280/d1vd6g
record_format openpolar
spelling ftdatacite:10.7280/d1vd6g 2024-02-04T09:55:49+01:00 Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning ... Mohajerani, Yara Jeong, Seongsu Scheuchl, Bernd Velicogna, Isabella Rignot, Eric Milillo, Pietro 2020 https://dx.doi.org/10.7280/d1vd6g https://datadryad.org/stash/dataset/doi:10.7280/D1VD6G en eng Dryad https://github.com/yaramohajerani/GL_learning https://github.com/yaramohajerani/GL_learning https://dx.doi.org/10.1038/s41598-021-84309-3 Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode cc0-1.0 FOS Earth and related environmental sciences Dataset dataset 2020 ftdatacite https://doi.org/10.7280/d1vd6g10.1038/s41598-021-84309-3 2024-01-05T04:51:50Z 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 ... Dataset Antarc* Antarctic Ice Sheet DataCite Metadata Store (German National Library of Science and Technology) Antarctic
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language English
topic FOS Earth and related environmental sciences
spellingShingle FOS Earth and related environmental sciences
Mohajerani, Yara
Jeong, Seongsu
Scheuchl, Bernd
Velicogna, Isabella
Rignot, Eric
Milillo, Pietro
Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning ...
topic_facet FOS Earth and related environmental sciences
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 ... : 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 ...
format Dataset
author Mohajerani, Yara
Jeong, Seongsu
Scheuchl, Bernd
Velicogna, Isabella
Rignot, Eric
Milillo, Pietro
author_facet Mohajerani, Yara
Jeong, Seongsu
Scheuchl, Bernd
Velicogna, Isabella
Rignot, Eric
Milillo, Pietro
author_sort Mohajerani, Yara
title Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning ...
title_short Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning ...
title_full Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning ...
title_fullStr Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning ...
title_full_unstemmed Automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning ...
title_sort automatic delineation of glacier grounding lines in differential interferometric synthetic-aperture radar data using deep learning ...
publisher Dryad
publishDate 2020
url https://dx.doi.org/10.7280/d1vd6g
https://datadryad.org/stash/dataset/doi:10.7280/D1VD6G
geographic Antarctic
geographic_facet Antarctic
genre Antarc*
Antarctic
Ice Sheet
genre_facet Antarc*
Antarctic
Ice Sheet
op_relation https://github.com/yaramohajerani/GL_learning
https://github.com/yaramohajerani/GL_learning
https://dx.doi.org/10.1038/s41598-021-84309-3
op_rights Creative Commons Zero v1.0 Universal
https://creativecommons.org/publicdomain/zero/1.0/legalcode
cc0-1.0
op_doi https://doi.org/10.7280/d1vd6g10.1038/s41598-021-84309-3
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