Articles | Volume 15, issue 11
The Cryosphere, 15, 5041–5059, 2021
https://doi.org/10.5194/tc-15-5041-2021
The Cryosphere, 15, 5041–5059, 2021
https://doi.org/10.5194/tc-15-5041-2021

Research article 01 Nov 2021

Research article | 01 Nov 2021

Image classification of marine-terminating outlet glaciers in Greenland using deep learning methods

Melanie Marochov et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (27 Mar 2021) by Bert Wouters
AR by Melanie Marochov on behalf of the Authors (07 May 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (26 May 2021) by Bert Wouters
RR by Anonymous Referee #2 (04 Jun 2021)
RR by Anonymous Referee #1 (06 Jun 2021)
RR by Anonymous Referee #3 (08 Jun 2021)
ED: Publish subject to revisions (further review by editor and referees) (22 Jun 2021) by Bert Wouters
AR by Melanie Marochov on behalf of the Authors (31 Aug 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to technical corrections (19 Sep 2021) by Bert Wouters
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Short summary
Research into the use of deep learning for pixel-level classification of landscapes containing marine-terminating glaciers is lacking. We adapt a novel and transferable deep learning workflow to classify satellite imagery containing marine-terminating outlet glaciers in Greenland. Our workflow achieves high accuracy and mimics human visual performance, potentially providing a useful tool to monitor glacier change and further understand the impacts of climate change in complex glacial settings.