Articles | Volume 17, issue 11
https://doi.org/10.5194/tc-17-4957-2023
© Author(s) 2023. 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-17-4957-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Out-of-the-box calving-front detection method using deep learning
Institute of Geography, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
Nora Gourmelon
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
Thorsten Seehaus
Institute of Geography, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
Andreas Maier
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
Johannes J. Fürst
Institute of Geography, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
Matthias H. Braun
Institute of Geography, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
Vincent Christlein
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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Vijaya Kumar Thota, Thorsten Seehaus, Friedrich Knuth, Amaury Dehecq, Christian Salewski, and Matthias Braun
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-490, https://doi.org/10.5194/essd-2025-490, 2025
Preprint under review for ESSD
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We studied past glacier changes in a rapidly warming Antarctic region with little historical data. Using approximately 2000 aerial photographs from the year 1989 over the western Antarctic Peninsula and nearby islands, we created detailed elevation models and orthoimages that have high accuracy compared to recent satellite data. This open dataset aids tracking historical ice loss and its role in sea level rise.
Angelika Humbert, Veit Helm, Ole Zeising, Niklas Neckel, Matthias H. Braun, Shfaqat Abbas Khan, Martin Rückamp, Holger Steeb, Julia Sohn, Matthias Bohnen, and Ralf Müller
The Cryosphere, 19, 3009–3032, https://doi.org/10.5194/tc-19-3009-2025, https://doi.org/10.5194/tc-19-3009-2025, 2025
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We study the evolution of a massive lake on the Greenland Ice Sheet using satellite and airborne data and some modelling. The lake is emptying rapidly. Water flows to the glacier's base through cracks and triangular-shaped moulins that remain visible over the years. Some of them become reactivated. We find features inside the glacier that stem from drainage events with a width of even 1 km. These features are persistent over the years, although they are changing in shape.
Katrina Lutz, Ilaria Tabone, Angelika Humbert, and Matthias Braun
The Cryosphere, 19, 2601–2614, https://doi.org/10.5194/tc-19-2601-2025, https://doi.org/10.5194/tc-19-2601-2025, 2025
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Supraglacial lakes develop from meltwater collecting on the surface of glaciers. These lakes can drain rapidly, discharging meltwater to the glacier bed. In this study, we assess the spatial and temporal distribution of rapid drainages in Northeast Greenland using optical satellite images. After comparing rapid drainage occurrence with several environmental and geophysical parameters, little indication of the influencing conditions for a rapid drainage was found.
Theresa Dobler, Wilfried Hagg, Martin Rückamp, Thorsten Seehaus, and Christoph Mayer
EGUsphere, https://doi.org/10.5194/egusphere-2025-2513, https://doi.org/10.5194/egusphere-2025-2513, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
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We studied how a glacier in the Austrian Alps moves more slowly over time due to climate change. By combining long-term field data with recent aerial images, we show how thinning reduce glacier flow. Standard satellite methods failed to detect this slow movement, so we used manual tracking to create a reliable map. Our findings help understand changes in glacier behavior in a warming climate.
Torsten Kanzow, Angelika Humbert, Thomas Mölg, Mirko Scheinert, Matthias Braun, Hans Burchard, Francesca Doglioni, Philipp Hochreuther, Martin Horwath, Oliver Huhn, Maria Kappelsberger, Jürgen Kusche, Erik Loebel, Katrina Lutz, Ben Marzeion, Rebecca McPherson, Mahdi Mohammadi-Aragh, Marco Möller, Carolyne Pickler, Markus Reinert, Monika Rhein, Martin Rückamp, Janin Schaffer, Muhammad Shafeeque, Sophie Stolzenberger, Ralph Timmermann, Jenny Turton, Claudia Wekerle, and Ole Zeising
The Cryosphere, 19, 1789–1824, https://doi.org/10.5194/tc-19-1789-2025, https://doi.org/10.5194/tc-19-1789-2025, 2025
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The Greenland Ice Sheet represents the second-largest contributor to global sea-level rise. We quantify atmosphere, ice and ocean processes related to the mass balance of glaciers in northeast Greenland, focusing on Greenland’s largest floating ice tongue, the 79° N Glacier. We find that together, the different in situ and remote sensing observations and model simulations reveal a consistent picture of a coupled atmosphere–ice sheet–ocean system that has entered a phase of major change.
Kaian Shahateet, Johannes J. Fürst, Francisco Navarro, Thorsten Seehaus, Daniel Farinotti, and Matthias Braun
The Cryosphere, 19, 1577–1597, https://doi.org/10.5194/tc-19-1577-2025, https://doi.org/10.5194/tc-19-1577-2025, 2025
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In the present work, we provide a new ice thickness reconstruction of the Antarctic Peninsula Ice Sheet north of 70º S using inversion modeling. This model consists of two steps: the first uses basic assumptions of the rheology of the glacier, and the second uses mass conservation to improve the reconstruction where the assumptions made previously are expected to fail. Validation with independent data showed that our reconstruction improved compared to other reconstructions that are available.
Akash M. Patil, Christoph Mayer, Thorsten Seehaus, and Alexander R. Groos
EGUsphere, https://doi.org/10.5194/egusphere-2025-615, https://doi.org/10.5194/egusphere-2025-615, 2025
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We studied how snow and ice layers form and change in the Aletsch Glacier using radar and simple models. Our research mapped these layers' density and tracked their history over 12 years. This helps improve the glacier mass balance estimates. Using non-invasive radar techniques and models, we offer a new way to understand glaciers' evolution under regional climate conditions.
Marcel Dreier, Moritz Koch, Nora Gourmelon, Norbert Blindow, Daniel Steinhage, Fei Wu, Thorsten Seehaus, Matthias Braun, Andreas Maier, and Vincent Christlein
EGUsphere, https://doi.org/10.5194/egusphere-2024-3597, https://doi.org/10.5194/egusphere-2024-3597, 2025
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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.
Felix Pfluger, Samuel Weber, Joseph Steinhauser, Christian Zangerl, Christine Fey, Johannes Fürst, and Michael Krautblatter
Earth Surf. Dynam., 13, 41–70, https://doi.org/10.5194/esurf-13-41-2025, https://doi.org/10.5194/esurf-13-41-2025, 2025
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Our study explores permafrost–glacier interactions with a focus on their implications for preparing or triggering high-volume rock slope failures. Using the Bliggspitze rock slide as a case study, we demonstrate a new type of rock slope failure mechanism triggered by the uplift of the cold–warm dividing line in polythermal alpine glaciers, a widespread and currently under-explored phenomenon in alpine environments worldwide.
Johannes Jakob Fürst, David Farías-Barahona, Thomas Bruckner, Lucia Scaff, Martin Mergili, Santiago Montserrat, and Humberto Peña
EGUsphere, https://doi.org/10.5194/egusphere-2024-3103, https://doi.org/10.5194/egusphere-2024-3103, 2025
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The 1987 Parraguirre ice-rock avalanche developed into a devastating debris-flow causing loss of many lives and inflicting severe damage near Santiago, Chile. Here, we revise this event combining various observational records with modelling techniques. In this year, important snow cover coincided with warm days in spring. We further quantify the total solid volume, and forward important upward corrections for the trigger and flood volumes. Finally, river damming was key for high flow mobility.
Katrina Lutz, Lily Bever, Christian Sommer, Thorsten Seehaus, Angelika Humbert, Mirko Scheinert, and Matthias Braun
The Cryosphere, 18, 5431–5449, https://doi.org/10.5194/tc-18-5431-2024, https://doi.org/10.5194/tc-18-5431-2024, 2024
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The estimation of the amount of water found within supraglacial lakes is important for understanding how much water is lost from glaciers each year. Here, we develop two new methods for estimating supraglacial lake volume that can be easily applied on a large scale. Furthermore, we compare these methods to two previously developed methods in order to determine when it is best to use each method. Finally, three of these methods are applied to peak melt dates over an area in Northeast Greenland.
Livia Piermattei, Michael Zemp, Christian Sommer, Fanny Brun, Matthias H. Braun, Liss M. Andreassen, Joaquín M. C. Belart, Etienne Berthier, Atanu Bhattacharya, Laura Boehm Vock, Tobias Bolch, Amaury Dehecq, Inés Dussaillant, Daniel Falaschi, Caitlyn Florentine, Dana Floricioiu, Christian Ginzler, Gregoire Guillet, Romain Hugonnet, Matthias Huss, Andreas Kääb, Owen King, Christoph Klug, Friedrich Knuth, Lukas Krieger, Jeff La Frenierre, Robert McNabb, Christopher McNeil, Rainer Prinz, Louis Sass, Thorsten Seehaus, David Shean, Désirée Treichler, Anja Wendt, and Ruitang Yang
The Cryosphere, 18, 3195–3230, https://doi.org/10.5194/tc-18-3195-2024, https://doi.org/10.5194/tc-18-3195-2024, 2024
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Satellites have made it possible to observe glacier elevation changes from all around the world. In the present study, we compared the results produced from two different types of satellite data between different research groups and against validation measurements from aeroplanes. We found a large spread between individual results but showed that the group ensemble can be used to reliably estimate glacier elevation changes and related errors from satellite data.
Anna Wendleder, Jasmin Bramboeck, Jamie Izzard, Thilo Erbertseder, Pablo d'Angelo, Andreas Schmitt, Duncan J. Quincey, Christoph Mayer, and Matthias H. Braun
The Cryosphere, 18, 1085–1103, https://doi.org/10.5194/tc-18-1085-2024, https://doi.org/10.5194/tc-18-1085-2024, 2024
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This study analyses the basal sliding and the hydrological drainage of Baltoro Glacier, Pakistan. The surface velocity was characterized by a spring speed-up, summer peak, and autumn speed-up. Snow melt has the largest impact on the spring speed-up, summer velocity peak, and the transition from inefficient to efficient drainage. Drainage from supraglacial lakes contributed to the fall speed-up. Increased summer temperatures will intensify the magnitude of meltwater and thus surface velocities.
Thorsten Seehaus, Christian Sommer, Thomas Dethinne, and Philipp Malz
The Cryosphere, 17, 4629–4644, https://doi.org/10.5194/tc-17-4629-2023, https://doi.org/10.5194/tc-17-4629-2023, 2023
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Existing mass budget estimates for the northern Antarctic Peninsula (>70° S) are affected by considerable limitations. We carried out the first region-wide analysis of geodetic mass balances throughout this region (coverage of 96.4 %) for the period 2013–2017 based on repeat pass bi-static TanDEM-X acquisitions. A total mass budget of −24.1±2.8 Gt/a is revealed. Imbalanced high ice discharge, particularly at former ice shelf tributaries, is the main driver of overall ice loss.
Alexandra M. Zuhr, Erik Loebel, Marek Muchow, Donovan Dennis, Luisa von Albedyll, Frigga Kruse, Heidemarie Kassens, Johanna Grabow, Dieter Piepenburg, Sören Brandt, Rainer Lehmann, Marlene Jessen, Friederike Krüger, Monika Kallfelz, Andreas Preußer, Matthias Braun, Thorsten Seehaus, Frank Lisker, Daniela Röhnert, and Mirko Scheinert
Polarforschung, 91, 73–80, https://doi.org/10.5194/polf-91-73-2023, https://doi.org/10.5194/polf-91-73-2023, 2023
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Polar research is an interdisciplinary and multi-faceted field of research. Its diversity ranges from history to geology and geophysics to social sciences and education. This article provides insights into the different areas of German polar research. This was made possible by a seminar series, POLARSTUNDE, established in the summer of 2020 and organized by the German Society of Polar Research and the German National Committee of the Association of Polar Early Career Scientists (APECS Germany).
Franziska Temme, David Farías-Barahona, Thorsten Seehaus, Ricardo Jaña, Jorge Arigony-Neto, Inti Gonzalez, Anselm Arndt, Tobias Sauter, Christoph Schneider, and Johannes J. Fürst
The Cryosphere, 17, 2343–2365, https://doi.org/10.5194/tc-17-2343-2023, https://doi.org/10.5194/tc-17-2343-2023, 2023
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Calibration of surface mass balance (SMB) models on regional scales is challenging. We investigate different calibration strategies with the goal of achieving realistic simulations of the SMB in the Monte Sarmiento Massif, Tierra del Fuego. Our results show that the use of regional observations from satellite data can improve the model performance. Furthermore, we compare four melt models of different complexity to understand the benefit of increasing the processes considered in the model.
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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Knowledge on the volume of glaciers is important to project future runoff. Here, we present a novel approach to reconstruct the regional ice thickness distribution from easily available remote-sensing data. We show that past ice thickness, derived from spaceborne glacier area and elevation datasets, can constrain the estimated ice thickness. Based on the unique glaciological database of the European Alps, the approach will be most beneficial in regions without direct thickness measurements.
Nora Gourmelon, Thorsten Seehaus, Matthias Braun, Andreas Maier, and Vincent Christlein
Earth Syst. Sci. Data, 14, 4287–4313, https://doi.org/10.5194/essd-14-4287-2022, https://doi.org/10.5194/essd-14-4287-2022, 2022
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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.
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.
Mirko Scheinert, Christoph Mayer, Martin Horwath, Matthias Braun, Anja Wendt, and Daniel Steinhage
Polarforschung, 89, 57–64, https://doi.org/10.5194/polf-89-57-2021, https://doi.org/10.5194/polf-89-57-2021, 2021
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Ice sheets, glaciers and further ice-covered areas with their changes as well as interactions with the solid Earth and the ocean are subject of intensive research, especially against the backdrop of global climate change. The resulting questions are of concern to scientists from various disciplines such as geodesy, glaciology, physical geography and geophysics. Thus, the working group "Polar Geodesy and Glaciology", founded in 2013, offers a forum for discussion and stimulating exchange.
Ethan Welty, Michael Zemp, Francisco Navarro, Matthias Huss, Johannes J. Fürst, Isabelle Gärtner-Roer, Johannes Landmann, Horst Machguth, Kathrin Naegeli, Liss M. Andreassen, Daniel Farinotti, Huilin Li, and GlaThiDa Contributors
Earth Syst. Sci. Data, 12, 3039–3055, https://doi.org/10.5194/essd-12-3039-2020, https://doi.org/10.5194/essd-12-3039-2020, 2020
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Knowing the thickness of glacier ice is critical for predicting the rate of glacier loss and the myriad downstream impacts. To facilitate forecasts of future change, we have added 3 million measurements to our worldwide database of glacier thickness: 14 % of global glacier area is now within 1 km of a thickness measurement (up from 6 %). To make it easier to update and monitor the quality of our database, we have used automated tools to check and track changes to the data over time.
Catrin Stadelmann, Johannes Jakob Fürst, Thomas Mölg, and Matthias Braun
The Cryosphere, 14, 3399–3406, https://doi.org/10.5194/tc-14-3399-2020, https://doi.org/10.5194/tc-14-3399-2020, 2020
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The glaciers on Kilimanjaro are unique indicators for climatic changes in the tropical midtroposphere of Africa. A history of severe glacier area loss raises concerns about an imminent future disappearance. Yet the remaining ice volume is not well known. Here, we reconstruct ice thickness maps for the two largest remaining ice bodies to assess the current glacier state. We believe that our approach could provide a means for a glacier-specific calibration of reconstructions on different scales.
Cited articles
Abolvardi, A. A., Hamey, L., and Ho-Shon, K.: UNET-Based Multi-Task Architecture for Brain Lesion Segmentation, in: Digital Image Computing: Techniques and Applications (DICTA), 1–7, https://doi.org/10.1109/DICTA51227.2020.9363397, 2020. a
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 Isbrse, Greenland, J. Geophys. Res.-Earth Surf., 115, F01005, https://doi.org/10.1029/2009JF001405, 2010. a
Amyar, A., Modzelewski, R., Li, H., and Ruan, S.: Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation, Comput. Biol. Med., 126, 104037, https://doi.org/10.1016/j.compbiomed.2020.104037, 2020. a, b
Baumhoer, C. A., Dietz, A. J., 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
Baumhoer, C. A., Dietz, A. J., Kneisel, C., Paeth, H., and Kuenzer, C.: Environmental drivers of circum-Antarctic glacier and ice shelf front retreat over the last two decades, The Cryosphere, 15, 2357–2381, https://doi.org/10.5194/tc-15-2357-2021, 2021. a
Baumhoer, C. A., Dietz, A. J., Heidler, K., and Kuenzer, C.: IceLines – A new data set of Antarctic ice shelf front positions, Sci. Data, 10, 138, https://doi.org/10.1038/s41597-023-02045-x, 2023. a
Beer, C., Biebow, N., Braun, M., Döring, N., Gaedicke, C., Gutt, J., Hagen, W., Hauck, J., Heinemann, G., Herata, H., Holfort, J., Jung, T., Kassens, H., Klenzendorf, S., Läufer, A., Lohmann, G., Nixdorf, U., Plass, S., Quillfeldt, P., Rhein, M., Rachold, V., Riedel, A., Sachs, T., and Wendisch, M.: Forschungsagenda Polarregionen im Wandel, 79, Bundesministerium für Bildung und Forschung (BMBF), Germany, 2021. a
Bischke, B., Helber, P., Folz, J., Borth, D., and Dengel, A.: Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks, in: International Conference on Image Processing (ICIP), 1480–1484, IEEE, Taipei, ISBN 978-1-5386-6249-6, https://doi.org/10.1109/ICIP.2019.8803050, 2019. a
Carr, J. R., Stokes, C., and Vieli, A.: Recent retreat of major outlet glaciers on Novaya Zemlya, Russian Arctic, influenced by fjord geometry and sea-ice conditions, J. Glaciol., 60, 155–170, https://doi.org/10.3189/2014JoG13J122, 2014. a
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H.: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation, in: European conference on computer vision (ECCV), edited by: Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y., 833–851, Springer International Publishing, Cham, ISBN 978-3-030-01234-2, https://doi.org/10.1007/978-3-030-01234-2_49, 2018. a
Chen, S., Bortsova, G., García-Uceda Juárez, A., van Tulder, G., and de Bruijne, M.: Multi-task Attention-Based Semi-supervised Learning for Medical Image Segmentation, in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, edited by: Shen, D., Liu, T., Peters, T. M., Staib, L. H., Essert, C., Zhou, S., Yap, P.-T., and Khan, A., Lecture Notes in Computer Science, 457–465, Springer International Publishing, Cham, ISBN 978-3-030-32248-9, https://doi.org/10.1007/978-3-030-32248-9_51, 2019. a, b
Cheng, D., Hayes, W., Larour, E., Mohajerani, Y., Wood, M., Velicogna, I., and Rignot, E.: Calving Front Machine (CALFIN): glacial termini dataset and automated deep learning extraction method for Greenland, 1972–2019, The Cryosphere, 15, 1663–1675, https://doi.org/10.5194/tc-15-1663-2021, 2021. a, b, c
Cook, A. J., Murray, T., Luckman, A., Vaughan, D. G., and Barrand, N. E.: A new 100-m Digital Elevation Model of the Antarctic Peninsula derived from ASTER Global DEM: methods and accuracy assessment, Earth Syst. Sci. Data, 4, 129–142, https://doi.org/10.5194/essd-4-129-2012, 2012. a
Davari, A., Baller, C., Seehaus, T., Braun, M., Maier, A., and Christlein, V.: Pixel-wise Distance Regression for Glacier Calving Front Detection and Segmentation, IEEE T. Geosci. Remote, 60, 1–10, https://doi.org/10.1109/TGRS.2022.3158591, 2022. a
Friedl, P., Seehaus, T. C., Wendt, A., Braun, M. H., and Höppner, K.: Recent dynamic changes on Fleming Glacier after the disintegration of Wordie Ice Shelf, Antarctic Peninsula, The Cryosphere, 12, 1347–1365, https://doi.org/10.5194/tc-12-1347-2018, 2018. a
Gourmelon, N., Seehaus, T., Braun, M., Maier, A., and Christlein, V.: Calving fronts and where to find them: a benchmark dataset and methodology for automatic glacier calving front extraction from synthetic aperture radar imagery, Earth Syst. Sci. Data, 14, 4287–4313, https://doi.org/10.5194/essd-14-4287-2022, 2022a. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r
Gourmelon, N., Seehaus, T., Braun, M. H., Maier, A., and Christlein, V.: CaFFe (CAlving Fronts and where to Find thEm: a benchmark dataset and methodology for automatic glacier calving front extraction from sar imagery), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.940950, 2022b. a, b, c, d, e, f
Hartmann, A., Davari, A., Seehaus, T., Braun, M., Maier, A., and Christlein, V.: Bayesian U-Net for Segmenting Glaciers in Sar Imagery, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 41, 3479–3482, https://doi.org/10.1109/IGARSS47720.2021.9554292, iSSN: 2153-7003, 2021. a, b
He, K., Lian, C., Zhang, B., Zhang, X., Cao, X., Nie, D., Gao, Y., Zhang, J., and Shen, D.: HF-UNet: Learning Hierarchically Inter-Task Relevance in Multi-Task U-Net for Accurate Prostate Segmentation in CT Images, IEEE T. Med. Imaging, 40, 2118–2128, https://doi.org/10.1109/TMI.2021.3072956, 2021. a
Heidler, K., Mou, L., Baumhoer, C., Dietz, A., and Zhu, X. X.: HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline, IEEE T. Geosci. Remote, 60, 1–14, https://doi.org/10.1109/TGRS.2021.3064606, 2021. a, b
Heller, N., Isensee, F., Maier-Hein, K. H., Hou, X., Xie, C., Li, F., Nan, Y., Mu, G., Lin, Z., Han, M., Yao, G., Gao, Y., Zhang, Y., Wang, Y., Hou, F., Yang, J., Xiong, G., Tian, J., Zhong, C., Ma, J., Rickman, J., Dean, J., Stai, B., Tejpaul, R., Oestreich, M., Blake, P., Kaluzniak, H., Raza, S., Rosenberg, J., Moore, K., Walczak, E., Rengel, Z., Edgerton, Z., Vasdev, R., Peterson, M., McSweeney, S., Peterson, S., Kalapara, A., Sathianathen, N., Papanikolopoulos, N., and Weight, C.: The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge, Med. Image Anal., 67, 101821, https://doi.org/10.1016/j.media.2020.101821, 2021. a
Herrmann, O.: Out-of-the-box calving front detection method using deep learning (Version 3), Zenodo [data set], https://doi.org/10.5281/zenodo.8379954, 2023a. a, b
Herrmann, O.: Pretrained_nnUNet_calvingfront_detection, Zenodo [code], https://doi.org/10.5281/zenodo.7837300, 2023b. a
Oskar Herrmann: nnUNet_calvingfront_detection, Zenodo [code], https://doi.org/10.5281/zenodo.10169965, 2023c. a
Herrmann, O. and Gourmelon, N.: nnUNet_calvingfront_detection, Zenodo [code], https://doi.org/10.5281/zenodo.10168770, 2023. a
Isensee, F.: nnU-Ne, GitHub [code], https://github.com/MIC-DKFZ/nnUNet/tree/nnunetv1 (last access: 21 November 23), 2019. a
Jang, H.-J. and Cho, K.-O.: Applications of deep learning for the analysis of medical data, Arch. Pharm. Res., 42, 492–504, https://doi.org/10.1007/s12272-019-01162-9, 2019. a
Kholiavchenko, M., Sirazitdinov, I., Kubrak, K., Badrutdinova, R., Kuleev, R., Yuan, Y., Vrtovec, T., and Ibragimov, B.: Contour-aware multi-label chest X-ray organ segmentation, Int. J. Comput. Ass. Rad., 15, 425–436, https://doi.org/10.1007/s11548-019-02115-9, 2020. a
Kingma, D. P. and Ba, J.: Adam: A method for stochastic optimization, arXiv preprint, arXiv:1412.6980, 2014. a
Kneib-Walter, A., Lüthi, M. P., Moreau, L., and Vieli, A.: Drivers of Recurring Seasonal Cycle of Glacier Calving Styles and Patterns, Front. Earth Sci., 9, 667717, https://doi.org/10.3389/feart.2021.667717, 2021. a
Li, X., Wang, Y., Tang, Q., Fan, Z., and Yu, J.: Dual U-Net for the Segmentation of Overlapping Glioma Nuclei, IEEE Access, 7, 84040–84052, https://doi.org/10.1109/ACCESS.2019.2924744, 2019. a, b
Loebel, E., Scheinert, M., Horwath, M., Heidler, K., Christmann, J., Phan, L. D., Humbert, A., and Zhu, X. X.: Extracting glacier calving fronts by deep learning: the benefit of multi-spectral, topographic and textural input features, IEEE T. Geosci. Remote, 60, 1–12, https://doi.org/10.1109/TGRS.2022.3208454, 2022. a, b, c
Marochov, M., Stokes, C. R., and Carbonneau, P. E.: Image classification of marine-terminating outlet glaciers in Greenland using deep learning methods, The Cryosphere, 15, 5041–5059, https://doi.org/10.5194/tc-15-5041-2021, 2021. a
McNabb, R. W., Hock, R., and Huss, M.: Variations in Alaska tidewater glacier frontal ablation, 1985–2013, J. Geophys. Res.-Earth Surf., 120, 120–136, https://doi.org/10.1002/2014JF003276, 2015. a
Minowa, M., Schaefer, M., Sugiyama, S., Sakakibara, D., and Skvarca, P.: Frontal ablation and mass loss of the Patagonian icefields, Earth Planet. Sc. Lett., 561, 116811, https://doi.org/10.1016/j.epsl.2021.116811, 2021. a
Mohajerani, Y., Wood, M., Velicogna, I., and Rignot, E.: Detection of Glacier Calving Margins with Convolutional Neural Networks: A Case Study, Remote Sens., 11, 74, https://doi.org/10.3390/rs11010074, 2019. a, b, c
Periyasamy, M., Davari, A., Seehaus, T., Braun, M., Maier, A., and Christlein, V.: How to Get the Most Out of U-Net for Glacier Calving Front Segmentation, IEEE J. Sel. Top. Appl. Earth Obs., 15, 1712–1723, https://doi.org/10.1109/JSTARS.2022.3148033, 2022. a
Recinos, B., Maussion, F., Rothenpieler, T., and Marzeion, B.: Impact of frontal ablation on the ice thickness estimation of marine-terminating glaciers in Alaska, The Cryosphere, 13, 2657–2672, https://doi.org/10.5194/tc-13-2657-2019, 2019. a, b
Recinos, B., Maussion, F., Noël, B., Möller, M., and Marzeion, B.: Calibration of a frontal ablation parameterisation applied to Greenland's peripheral calving glaciers, J. Glaciol., 67, 1177–1189, https://doi.org/10.1017/jog.2021.63, 2021. a
Robel, A. A., Schoof, C., and Tziperman, E.: Persistence and variability of ice-stream grounding lines on retrograde bed slopes, The Cryosphere, 10, 1883–1896, https://doi.org/10.5194/tc-10-1883-2016, 2016. a
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation, in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, edited by: Navab, N., Hornegger, J., Wells, W. M., and Frangi, A. F., 9351, 234–241, Springer International Publishing, Cham, ISBN 978-3-319-24573-7, https://doi.org/10.1007/978-3-319-24574-4_28, 2015. a
Rott, H., Wuite, J., Rydt, J. D., Gudmundsson, G. H., Floricioiu, D., and Rack, W.: Impact of marine processes on flow dynamics of northern Antarctic Peninsula outlet glaciers, Nat. Commun., 11, 2969, https://doi.org/10.1038/s41467-020-16658-y, 2020. a
Shepherd, A., Ivins, E., Rignot, E., Smith, B., Broeke, M. V. D., Velicogna, I., Whitehouse, P., Briggs, K., Joughin, I., Krinner, G., Nowicki, S., Payne, T., Scambos, T., Schlegel, N., Geruo, A., Agosta, C., Ahlstrøm, A., Babonis, G., Barletta, V., Blazquez, A., Bonin, J., Csatho, B., Cullather, R., Felikson, D., Fettweis, X., Forsberg, R., Gallee, H., Gardner, A., Gilbert, L., Groh, A., Gunter, B., Hanna, E., Harig, C., Helm, V., Horvath, A., Horwath, M., Khan, S., Kjeldsen, K. K., Konrad, H., Langen, P., Lecavalier, B., Loomis, B., Luthcke, S., McMillan, M., Melini, D., Mernild, S., Mohajerani, Y., Moore, P., Mouginot, J., Moyano, G., Muir, A., Nagler, T., Nield, G., Nilsson, J., Noel, B., Otosaka, I., Pattle, M. E., Peltier, W. R., Pie, N., Rietbroek, R., Rott, H., Sandberg-Sørensen, L., Sasgen, I., Save, H., Scheuchl, B., Schrama, E., Schröder, L., Seo, K. W., Simonsen, S., Slater, T., Spada, G., Sutterley, T., Talpe, M., Tarasov, L., Berg, W. J. V. D., Wal, W. V. D., Wessem, M. V., Vishwakarma, B. D., Wiese, D., and Wouters, B.: Mass balance of the Antarctic Ice Sheet from 1992 to 2017, Nature, 558, 219–222, https://doi.org/10.1038/s41586-018-0179-y, 2018. a
Smith, L. N.: Cyclical Learning Rates for Training Neural Networks, in: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), 24–31 March 2017, Sanra Rosa, CA, USA, 464–472, https://doi.org/10.1109/WACV.2017.58, 2017. a
Straneo, F., Heimbach, P., Sergienko, O., Hamilton, G., Catania, G., Griffies, S., Hallberg, R., Jenkins, A., Joughin, I., Motyka, R., Pfeffer, W. T., Price, S. F., Rignot, E., Scambos, T., Truffer, M., and Vieli, A.: Challenges to Understanding the Dynamic Response of Greenland's Marine Terminating Glaciers to Oceanic and Atmospheric Forcing, B. Am. Meteorol. Soc., 94, 1131–1144, https://doi.org/10.1175/BAMS-D-12-00100.1, 2013. a
Zhang, E., Liu, L., and Huang, L.: Automatically delineating the calving front of Jakobshavn Isbræ from multitemporal TerraSAR-X images: a deep learning approach, The Cryosphere, 13, 1729–1741, https://doi.org/10.5194/tc-13-1729-2019, 2019. a, b, c
Zhang, E., Liu, L., Huang, L., and Ng, K. S.: An automated, generalized, deep-learning-based method for delineating the calving fronts of Greenland glaciers from multi-sensor remote sensing imagery, Remote Sens. Environ., 254, 112265, https://doi.org/10.1016/j.rse.2020.112265, 2021. a, b
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Delineating calving fronts of marine-terminating glaciers in satellite images is a labour-intensive task. We propose a method based on deep learning that automates this task. We choose a deep learning framework that adapts to any given dataset without needing deep learning expertise. The method is evaluated on a benchmark dataset for calving-front detection and glacier zone segmentation. The framework can beat the benchmark baseline without major modifications.
Delineating calving fronts of marine-terminating glaciers in satellite images is a...