Comparability of Deep Learning Techniques for Calving Front Segmentation in SAR Imagery

Gourmelon N, Seehaus T, Braun M, Maier A, Christlein V (2023)

Publication Status: Published

Publication Type: Conference contribution, Abstract of lecture

Future Publication Type: Conference contribution

Publication year: 2023

Event location: Vienna AT

URI: https://meetingorganizer.copernicus.org/EGU23/EGU23-5069.html

DOI: 10.5194/egusphere-egu23-5069

Authors with CRIS profile

Nora Gourmelon Lehrstuhl für Informatik 5 (Mustererkennung) Thorsten Seehaus Institut für Geographie Matthias Braun Professur für Geographie (Fernerkundung und GIS) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung) Vincent Christlein Lehrstuhl für Informatik 5 (Mustererkennung)

Related research project(s)

International Doctoral Program: Measuring and Modelling Mountain glaciers and ice caps in a Changing Climate (M³OCCA) (MOCCA) June 1, 2022 - May 31, 2026 Tapping the potential of Earth Observations (TAPE) April 1, 2019 - March 31, 2022

How to cite

APA:

Gourmelon, N., Seehaus, T., Braun, M., Maier, A., & Christlein, V. (2023). Comparability of Deep Learning Techniques for Calving Front Segmentation in SAR Imagery. Paper presentation at European Geoscience Union General Assembly 2023, Vienna, AT.

MLA:

Gourmelon, Nora, et al. "Comparability of Deep Learning Techniques for Calving Front Segmentation in SAR Imagery." Presented at European Geoscience Union General Assembly 2023, Vienna 2023.

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