Articles | Volume 14, issue 9
https://doi.org/10.5194/essd-14-4287-2022
https://doi.org/10.5194/essd-14-4287-2022
Data description paper
 | 
22 Sep 2022
Data description paper |  | 22 Sep 2022

Calving fronts and where to find them: a benchmark dataset and methodology for automatic glacier calving front extraction from synthetic aperture radar imagery

Nora Gourmelon, Thorsten Seehaus, Matthias Braun, Andreas Maier, and Vincent Christlein

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Cited articles

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Amundson, J. M., Fahnestock, M., Truffer, M., Brown, J., Lüthi, M. P., and Motyka, R. J.: Ice mélange dynamics and implications for terminus stability, Jakobshavn Isbræ, Greenland, J. Geophys. Res., 115, F01005, https://doi.org/10.1029/2009JF001405, 2010. a
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Baumhoer, C., Dietz, A., Dech, S., and Kuenzer, C.: Remote Sensing of Antarctic Glacier and Ice-Shelf Front Dynamics – A Review, Remote Sens., 10, 1445, https://doi.org/10.3390/rs10091445, 2018. a, b
Baumhoer, C. A., Dietz, A. J., Kneisel, C., and Kuenzer, C.: Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning, Remote Sens., 11, 2529, https://doi.org/10.3390/rs11212529, 2019. a, b, c, d, e, f, g, h
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Short summary
Ice loss of glaciers shows in retreating calving fronts (i.e., the position where icebergs break off the glacier and drift into the ocean). This paper presents a benchmark dataset for calving front delineation in synthetic aperture radar (SAR) images. The dataset can be used to train and test deep learning techniques, which automate the monitoring of the calving front. Provided example models achieve front delineations with an average distance of 887 m to the correct calving front.
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