Articles | Volume 15, issue 11
https://doi.org/10.5194/tc-15-5041-2021
© Author(s) 2021. 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-15-5041-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Nov 2021
Research article |
| 01 Nov 2021
| 01 Nov 2021
Image classification of marine-terminating outlet glaciers in Greenland using deep learning methods
Melanie Marochov
Department of Geography, Durham University, Durham, DH1 3LE, UK
Chris R. Stokes
Department of Geography, Durham University, Durham, DH1 3LE, UK
Patrice E. Carbonneau
CORRESPONDING AUTHOR
Department of Geography, Durham University, Durham, DH1 3LE, UK
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Cited
29 citations as recorded by crossref.
- An AI approach to operationalise global daily PlanetScope satellite imagery for river water masking S. Valman et al.
- A new time-stamped glacier inventory of High Mountain Asia based on deep learning and remote sensing big data in 2020 C. Ke et al.
- SAU-Net: A Deep Learning Approach for Glacier Mapping Based on Multisource Remote Sensing Data Y. Xiang et al.
- Elasticnet-Based Vision Transformers for early detection of Parkinson’s disease E. Yüzgeç Özdemir & F. Özyurt
- Calving fronts and where to find them: a benchmark dataset and methodology for automatic glacier calving front extraction from synthetic aperture radar imagery N. Gourmelon et al.
- AutoTerm: an automated pipeline for glacier terminus extraction using machine learning and a “big data” repository of Greenland glacier termini E. Zhang et al.
- Calving front monitoring at a subseasonal resolution: a deep learning application for Greenland glaciers E. Loebel et al.
- A Deep Active Contour Model for Delineating Glacier Calving Fronts K. Heidler et al.
- Area, volume and ELA changes of West Greenland local glaciers and ice caps from 1985–2020 A. Securo et al.
- An integrated deep learning and object-based image analysis approach for mapping debris-covered glaciers D. Thomas et al.
- SSL4SAR: Self-Supervised Learning for Glacier Calving Front Extraction From SAR Imagery N. Gourmelon et al.
- Demystifying image-based machine learning: a practical guide to automated analysis of field imagery using modern machine learning tools B. Belcher et al.
- 基于深度学习和遥感大数据的亚洲高山区2020年有时间戳标记的冰川编目 长. 柯 et al.
- Glacier extraction based on high-spatial-resolution remote-sensing images using a deep-learning approach with attention mechanism X. Chu et al.
- AMD-HookNet for Glacier Front Segmentation F. Wu et al.
- Contextual HookFormer for Glacier Calving Front Segmentation F. Wu et al.
- Mapping taluses using deep learning and high-resolution satellite images D. Jiang et al.
- Ensemble-Based Early Detection of Malaria via Explainable ViT-CNN Feature Fusion and SHAP E. Özdemir et al.
- A Scoping Review of Automated Calving Front Detection in Satellite Images and Calving Front Position Datasets W. Milczarek et al.
- Accurate and automatic mapping of complex debris‐covered glacier from remote sensing imagery using deep convolutional networks R. Lin et al.
- Glacier Retreating Analysis on the Southeastern Tibetan Plateau via Multisource Remote Sensing Data Y. Xiao et al.
- Automated Delineation of Supraglacial Debris Cover Using Deep Learning and Multisource Remote Sensing Data S. Kaushik et al.
- Mapping Debris-Covered Glaciers Using High-Resolution Imagery (GF-2) and Deep Learning Algorithms X. Yang et al.
- Advances in monitoring glaciological processes in Kalallit Nunaat (Greenland) over the past decades D. Fahrner et al.
- 12 Years of Area Variation by the Drygalski Ice Tongue as Measured With COSMO-SkyMed M. Moctezuma-Flores et al.
- AMD-HookNet++: Evolution of AMD-HookNet With Hybrid CNN–Transformer Feature Enhancement for Glacier Calving Front Segmentation F. Wu et al.
- 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 E. Loebel et al.
- Out-of-the-box calving-front detection method using deep learning O. Herrmann et al.
- Long Time-Series Glacier Outlines in the Three-Rivers Headwater Region From 1986 to 2021 Based on Deep Learning L. Chen et al.
29 citations as recorded by crossref.
- An AI approach to operationalise global daily PlanetScope satellite imagery for river water masking S. Valman et al.
- A new time-stamped glacier inventory of High Mountain Asia based on deep learning and remote sensing big data in 2020 C. Ke et al.
- SAU-Net: A Deep Learning Approach for Glacier Mapping Based on Multisource Remote Sensing Data Y. Xiang et al.
- Elasticnet-Based Vision Transformers for early detection of Parkinson’s disease E. Yüzgeç Özdemir & F. Özyurt
- Calving fronts and where to find them: a benchmark dataset and methodology for automatic glacier calving front extraction from synthetic aperture radar imagery N. Gourmelon et al.
- AutoTerm: an automated pipeline for glacier terminus extraction using machine learning and a “big data” repository of Greenland glacier termini E. Zhang et al.
- Calving front monitoring at a subseasonal resolution: a deep learning application for Greenland glaciers E. Loebel et al.
- A Deep Active Contour Model for Delineating Glacier Calving Fronts K. Heidler et al.
- Area, volume and ELA changes of West Greenland local glaciers and ice caps from 1985–2020 A. Securo et al.
- An integrated deep learning and object-based image analysis approach for mapping debris-covered glaciers D. Thomas et al.
- SSL4SAR: Self-Supervised Learning for Glacier Calving Front Extraction From SAR Imagery N. Gourmelon et al.
- Demystifying image-based machine learning: a practical guide to automated analysis of field imagery using modern machine learning tools B. Belcher et al.
- 基于深度学习和遥感大数据的亚洲高山区2020年有时间戳标记的冰川编目 长. 柯 et al.
- Glacier extraction based on high-spatial-resolution remote-sensing images using a deep-learning approach with attention mechanism X. Chu et al.
- AMD-HookNet for Glacier Front Segmentation F. Wu et al.
- Contextual HookFormer for Glacier Calving Front Segmentation F. Wu et al.
- Mapping taluses using deep learning and high-resolution satellite images D. Jiang et al.
- Ensemble-Based Early Detection of Malaria via Explainable ViT-CNN Feature Fusion and SHAP E. Özdemir et al.
- A Scoping Review of Automated Calving Front Detection in Satellite Images and Calving Front Position Datasets W. Milczarek et al.
- Accurate and automatic mapping of complex debris‐covered glacier from remote sensing imagery using deep convolutional networks R. Lin et al.
- Glacier Retreating Analysis on the Southeastern Tibetan Plateau via Multisource Remote Sensing Data Y. Xiao et al.
- Automated Delineation of Supraglacial Debris Cover Using Deep Learning and Multisource Remote Sensing Data S. Kaushik et al.
- Mapping Debris-Covered Glaciers Using High-Resolution Imagery (GF-2) and Deep Learning Algorithms X. Yang et al.
- Advances in monitoring glaciological processes in Kalallit Nunaat (Greenland) over the past decades D. Fahrner et al.
- 12 Years of Area Variation by the Drygalski Ice Tongue as Measured With COSMO-SkyMed M. Moctezuma-Flores et al.
- AMD-HookNet++: Evolution of AMD-HookNet With Hybrid CNN–Transformer Feature Enhancement for Glacier Calving Front Segmentation F. Wu et al.
- 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 E. Loebel et al.
- Out-of-the-box calving-front detection method using deep learning O. Herrmann et al.
- Long Time-Series Glacier Outlines in the Three-Rivers Headwater Region From 1986 to 2021 Based on Deep Learning L. Chen et al.
Saved (final revised paper)
Latest update: 04 May 2026
Short summary
Research into the use of deep learning for pixel-level classification of landscapes containing marine-terminating glaciers is lacking. We adapt a novel and transferable deep learning workflow to classify satellite imagery containing marine-terminating outlet glaciers in Greenland. Our workflow achieves high accuracy and mimics human visual performance, potentially providing a useful tool to monitor glacier change and further understand the impacts of climate change in complex glacial settings.
Research into the use of deep learning for pixel-level classification of landscapes containing...
