Articles | Volume 19, issue 11
https://doi.org/10.5194/tc-19-5337-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-5337-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Nov 2025
Research article |
| 04 Nov 2025
| 04 Nov 2025
IceAnatomy: a benchmark dataset and methodology for automatic ice boundary extraction from radio-echo sounding data
Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
Moritz Koch
Institut für Geographie, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
Nora Gourmelon
Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
Norbert Blindow
Institut für Geographie, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
Daniel Steinhage
Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany
Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
Thorsten Seehaus
Institut für Geographie, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
Matthias Braun
Institut für Geographie, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
Andreas Maier
Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
Vincent Christlein
Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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Christian Sommer, Johannes J. Fürst, Matthias Huss, and Matthias H. Braun
The Cryosphere, 17, 2285–2303, https://doi.org/10.5194/tc-17-2285-2023, https://doi.org/10.5194/tc-17-2285-2023, 2023
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Angelika Humbert, Julia Christmann, Hugh F. J. Corr, Veit Helm, Lea-Sophie Höyns, Coen Hofstede, Ralf Müller, Niklas Neckel, Keith W. Nicholls, Timm Schultz, Daniel Steinhage, Michael Wolovick, and Ole Zeising
The Cryosphere, 16, 4107–4139, https://doi.org/10.5194/tc-16-4107-2022, https://doi.org/10.5194/tc-16-4107-2022, 2022
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Remote-sensing-derived basal melt rates of ice shelves are of great importance due to their capability to cover larger areas. We performed in situ measurements with a phase-sensitive radar on the southern Filchner Ice Shelf, showing moderate melt rates and low small-scale spatial variability. The comparison with remote-sensing-based melt rates revealed large differences caused by the estimation of vertical strain rates from remote sensing velocity fields that modern fields can overcome.
Steven Franke, Daniela Jansen, Tobias Binder, John D. Paden, Nils Dörr, Tamara A. Gerber, Heinrich Miller, Dorthe Dahl-Jensen, Veit Helm, Daniel Steinhage, Ilka Weikusat, Frank Wilhelms, and Olaf Eisen
Earth Syst. Sci. Data, 14, 763–779, https://doi.org/10.5194/essd-14-763-2022, https://doi.org/10.5194/essd-14-763-2022, 2022
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The Northeast Greenland Ice Stream (NEGIS) is the largest ice stream in Greenland. In order to better understand the past and future dynamics of the NEGIS, we present a high-resolution airborne radar data set (EGRIP-NOR-2018) for the onset region of the NEGIS. The survey area is centered at the location of the drill site of the East Greenland Ice-Core Project (EastGRIP), and radar profiles cover both shear margins and are aligned parallel to several flow lines.
Christian Sommer, Thorsten Seehaus, Andrey Glazovsky, and Matthias H. Braun
The Cryosphere, 16, 35–42, https://doi.org/10.5194/tc-16-35-2022, https://doi.org/10.5194/tc-16-35-2022, 2022
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Arctic glaciers have been subject to extensive warming due to global climate change, yet their contribution to sea level rise has been relatively small in the past. In this study we provide mass changes of most glaciers of the Russian High Arctic (Franz Josef Land, Severnaya Zemlya, Novaya Zemlya). We use TanDEM-X satellite measurements to derive glacier surface elevation changes. Our results show an increase in glacier mass loss and a sea level rise contribution of 0.06 mm/a (2010–2017).
Peter Friedl, Thorsten Seehaus, and Matthias Braun
Earth Syst. Sci. Data, 13, 4653–4675, https://doi.org/10.5194/essd-13-4653-2021, https://doi.org/10.5194/essd-13-4653-2021, 2021
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Consistent and continuous data on glacier surface velocity are important inputs to time series analyses, numerical ice dynamic modeling and glacier mass flux computations. We present a new data set of glacier surface velocities derived from Sentinel-1 radar satellite data that covers 12 major glaciated regions outside the polar ice sheets. The data comprise continuously updated scene-pair velocity fields, as well as monthly and annually averaged velocity mosaics at 200 m spatial resolution.
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Short summary
In this paper, we present a ready-to-use benchmark dataset to train machine learning approaches for detecting ice thickness from radar data. It includes radargrams of glaciers and ice sheets alongside annotations for their air–ice and ice–bedrock boundary. Furthermore, we introduce a baseline model and evaluate the influence of several geographical and glaciological factors on the performance of our model.
In this paper, we present a ready-to-use benchmark dataset to train machine learning approaches...
