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
18 citations as recorded by crossref.
- An AI approach to operationalise global daily PlanetScope satellite imagery for river water masking S. Valman et al. 10.1016/j.rse.2023.113932
- Automated Delineation of Supraglacial Debris Cover Using Deep Learning and Multisource Remote Sensing Data S. Kaushik et al. 10.3390/rs14061352
- Mapping Debris-Covered Glaciers Using High-Resolution Imagery (GF-2) and Deep Learning Algorithms X. Yang et al. 10.3390/rs16122062
- Advances in monitoring glaciological processes in Kalallit Nunaat (Greenland) over the past decades D. Fahrner et al. 10.1371/journal.pclm.0000379
- 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. 10.5194/essd-14-4287-2022
- AutoTerm: an automated pipeline for glacier terminus extraction using machine learning and a “big data” repository of Greenland glacier termini E. Zhang et al. 10.5194/tc-17-3485-2023
- 12 Years of Area Variation by the Drygalski Ice Tongue as Measured With COSMO-SkyMed M. Moctezuma-Flores et al. 10.1109/JSTARS.2022.3205560
- A Deep Active Contour Model for Delineating Glacier Calving Fronts K. Heidler et al. 10.1109/TGRS.2023.3296539
- An integrated deep learning and object-based image analysis approach for mapping debris-covered glaciers D. Thomas et al. 10.3389/frsen.2023.1161530
- Demystifying image-based machine learning: a practical guide to automated analysis of field imagery using modern machine learning tools B. Belcher et al. 10.3389/fmars.2023.1157370
- Glacier extraction based on high-spatial-resolution remote-sensing images using a deep-learning approach with attention mechanism X. Chu et al. 10.5194/tc-16-4273-2022
- AMD-HookNet for Glacier Front Segmentation F. Wu et al. 10.1109/TGRS.2023.3245419
- Out-of-the-box calving-front detection method using deep learning O. Herrmann et al. 10.5194/tc-17-4957-2023
- Contextual HookFormer for Glacier Calving Front Segmentation F. Wu et al. 10.1109/TGRS.2024.3368215
- Long Time-Series Glacier Outlines in the Three-Rivers Headwater Region From 1986 to 2021 Based on Deep Learning L. Chen et al. 10.1109/JSTARS.2022.3189277
- Accurate and automatic mapping of complex debris‐covered glacier from remote sensing imagery using deep convolutional networks R. Lin et al. 10.1002/gj.4615
- Glacier Retreating Analysis on the Southeastern Tibetan Plateau via Multisource Remote Sensing Data Y. Xiao et al. 10.1109/JSTARS.2023.3243771
- Novel Machine Learning Method Integrating Ensemble Learning and Deep Learning for Mapping Debris-Covered Glaciers Y. Lu et al. 10.3390/rs13132595
17 citations as recorded by crossref.
- An AI approach to operationalise global daily PlanetScope satellite imagery for river water masking S. Valman et al. 10.1016/j.rse.2023.113932
- Automated Delineation of Supraglacial Debris Cover Using Deep Learning and Multisource Remote Sensing Data S. Kaushik et al. 10.3390/rs14061352
- Mapping Debris-Covered Glaciers Using High-Resolution Imagery (GF-2) and Deep Learning Algorithms X. Yang et al. 10.3390/rs16122062
- Advances in monitoring glaciological processes in Kalallit Nunaat (Greenland) over the past decades D. Fahrner et al. 10.1371/journal.pclm.0000379
- 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. 10.5194/essd-14-4287-2022
- AutoTerm: an automated pipeline for glacier terminus extraction using machine learning and a “big data” repository of Greenland glacier termini E. Zhang et al. 10.5194/tc-17-3485-2023
- 12 Years of Area Variation by the Drygalski Ice Tongue as Measured With COSMO-SkyMed M. Moctezuma-Flores et al. 10.1109/JSTARS.2022.3205560
- A Deep Active Contour Model for Delineating Glacier Calving Fronts K. Heidler et al. 10.1109/TGRS.2023.3296539
- An integrated deep learning and object-based image analysis approach for mapping debris-covered glaciers D. Thomas et al. 10.3389/frsen.2023.1161530
- Demystifying image-based machine learning: a practical guide to automated analysis of field imagery using modern machine learning tools B. Belcher et al. 10.3389/fmars.2023.1157370
- Glacier extraction based on high-spatial-resolution remote-sensing images using a deep-learning approach with attention mechanism X. Chu et al. 10.5194/tc-16-4273-2022
- AMD-HookNet for Glacier Front Segmentation F. Wu et al. 10.1109/TGRS.2023.3245419
- Out-of-the-box calving-front detection method using deep learning O. Herrmann et al. 10.5194/tc-17-4957-2023
- Contextual HookFormer for Glacier Calving Front Segmentation F. Wu et al. 10.1109/TGRS.2024.3368215
- Long Time-Series Glacier Outlines in the Three-Rivers Headwater Region From 1986 to 2021 Based on Deep Learning L. Chen et al. 10.1109/JSTARS.2022.3189277
- Accurate and automatic mapping of complex debris‐covered glacier from remote sensing imagery using deep convolutional networks R. Lin et al. 10.1002/gj.4615
- Glacier Retreating Analysis on the Southeastern Tibetan Plateau via Multisource Remote Sensing Data Y. Xiao et al. 10.1109/JSTARS.2023.3243771
1 citations as recorded by crossref.
Latest update: 29 Jun 2024
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...
