Articles | Volume 17, issue 1
https://doi.org/10.5194/essd-17-65-2025
https://doi.org/10.5194/essd-17-65-2025
Data description paper
 | 
10 Jan 2025
Data description paper |  | 10 Jan 2025

Calving front positions for 42 key glaciers of the Antarctic Peninsula Ice Sheet: a sub-seasonal record from 2013 to 2023 based on deep-learning application to Landsat multi-spectral imagery

Erik Loebel, Celia A. Baumhoer, Andreas Dietz, Mirko Scheinert, and Martin Horwath

Data sets

Glacier calving front locations for the Antarctic Peninsula Ice Sheet derived from remote sensing and deep learning from 2013 to 2023 Erik Loebel et al. https://doi.org/10.1594/PANGAEA.963725

Manually delineated glacier calving front locations of 27 marine-terminating glaciers from 2013 to 2021 Erik Loebel et al. https://doi.org/10.25532/OPARA-282

Manually delineated glacier calving front locations of 20 marine-terminating glaciers of the Antarctic Peninsula from 2013 to 2023 Erik Loebel et al. https://doi.org/10.25532/OPARA-581

Terminus area change of 42 key glaciers of the Antarctic Peninsula from 2013 to 2023 derived from remote sensing and deep learning Erik Loebel et al. https://doi.org/10.25532/OPARA-557

Model code and software

eloebel/glacier-front-extraction: Initial release v1.0.0 Erik Loebel https://doi.org/10.5281/zenodo.7755774

eloebel/rectilinear-box-method: Initial release v1.0.0 Erik Loebel https://doi.org/10.5281/zenodo.7738605

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Short summary
Glacier calving front positions are important for understanding glacier dynamics and constraining ice modelling. We apply a deep-learning framework to multi-spectral Landsat imagery to create a calving front record for 42 key outlet glaciers of the Antarctic Peninsula Ice Sheet. The resulting data product includes 4817 calving front locations from 2013 to 2023 and achieves sub-seasonal temporal resolution.
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