Articles | Volume 13, issue 6
https://doi.org/10.5194/tc-13-1729-2019
© Author(s) 2019. 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-13-1729-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Jun 2019
Research article |
| 28 Jun 2019
| 28 Jun 2019
Automatically delineating the calving front of Jakobshavn Isbræ from multitemporal TerraSAR-X images: a deep learning approach
Earth System Science Programme, Faculty of Science, The Chinese
University of Hong Kong, Hong Kong, China
Earth System Science Programme, Faculty of Science, The Chinese
University of Hong Kong, Hong Kong, China
Lingcao Huang
Earth System Science Programme, Faculty of Science, The Chinese
University of Hong Kong, Hong Kong, China
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46 citations as recorded by crossref.
- Deep learning speeds up ice flow modelling by several orders of magnitude G. Jouvet et al. 10.1017/jog.2021.120
- Automated glacier extraction using a Transformer based deep learning approach from multi-sensor remote sensing imagery Y. Peng et al. 10.1016/j.isprsjprs.2023.06.015
- Meteorological Conditions and Cloud Effects on Surface Radiation Balance Near Helheim Glacier and Jakobshavn Isbræ (Greenland) Using Ground-Based Observations G. Djoumna et al. 10.3389/feart.2020.616105
- AMD-HookNet for Glacier Front Segmentation F. Wu et al. 10.1109/TGRS.2023.3245419
- A high-resolution calving front data product for marine-terminating glaciers in Svalbard T. Li et al. 10.5194/essd-16-919-2024
- Mapping the extent of giant Antarctic icebergs with deep learning A. Braakmann-Folgmann et al. 10.5194/tc-17-4675-2023
- Investigating the dynamics and interactions of surface features on Pine Island Glacier using remote sensing and deep learning Q. Zhu et al. 10.1016/j.accre.2024.07.011
- 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
- Significant changes of area, length and terminus of Sikkim Himalayan glaciers within the Kanchenjunga national park from 1990 - 2022 S. Saha et al. 10.1016/j.rines.2023.100013
- Observing traveling waves in glaciers with remote sensing: new flexible time series methods and application to Sermeq Kujalleq (Jakobshavn Isbræ), Greenland B. Riel et al. 10.5194/tc-15-407-2021
- How to Get the Most Out of U-Net for Glacier Calving Front Segmentation M. Periyasamy et al. 10.1109/JSTARS.2022.3148033
- Calving Front Machine (CALFIN): glacial termini dataset and automated deep learning extraction method for Greenland, 1972–2019 D. Cheng et al. 10.5194/tc-15-1663-2021
- Contrasting regional variability of buried meltwater extent over 2 years across the Greenland Ice Sheet D. Dunmire et al. 10.5194/tc-15-2983-2021
- A Multiscale Joint Deep Neural Network for Glacier Contour Extraction J. Liu et al. 10.1080/07038992.2021.1986810
- Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques R. Anilkumar et al. 10.5194/tc-17-2811-2023
- Application of a regularised Coulomb sliding law to Jakobshavn Isbræ, western Greenland M. Trevers et al. 10.5194/tc-18-5101-2024
- ATeX: A Benchmark for Image Classification of Water in Different Waterbodies Using Deep Learning Approaches S. Erfani & E. Goharian 10.1061/(ASCE)WR.1943-5452.0001615
- Seasonal Tidewater Glacier Terminus Oscillations Bias Multi‐Decadal Projections of Ice Mass Change D. Felikson et al. 10.1029/2021JF006249
- Automatic calving front extraction from digital elevation model-derived data Y. Dong et al. 10.1016/j.rse.2021.112854
- Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks Y. Kim et al. 10.5194/tc-14-1083-2020
- IceLines – A new data set of Antarctic ice shelf front positions C. Baumhoer et al. 10.1038/s41597-023-02045-x
- On Mathews Correlation Coefficient and Improved Distance Map Loss for Automatic Glacier Calving Front Segmentation in SAR Imagery A. Davari et al. 10.1109/TGRS.2021.3115883
- Image classification of marine-terminating outlet glaciers in Greenland using deep learning methods M. Marochov et al. 10.5194/tc-15-5041-2021
- TermPicks: a century of Greenland glacier terminus data for use in scientific and machine learning applications S. Goliber et al. 10.5194/tc-16-3215-2022
- 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
- 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
- A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations D. Aboelyazeed et al. 10.5194/bg-20-2671-2023
- Automatic Extraction of the Calving Front of Pine Island Glacier Based on Neural Network X. Song et al. 10.3390/rs15215168
- Combining TerraSAR-X and time-lapse photography for seasonal sea ice monitoring: the case of Deception Bay, Nunavik S. Dufour-Beauséjour et al. 10.5194/tc-14-1595-2020
- Semantic segmentation of glaciological features across multiple remote sensing platforms with the Segment Anything Model (SAM) S. Shankar et al. 10.1017/jog.2023.95
- LaeNet: A Novel Lightweight Multitask CNN for Automatically Extracting Lake Area and Shoreline from Remote Sensing Images W. Liu et al. 10.3390/rs13010056
- An automated, generalized, deep-learning-based method for delineating the calving fronts of Greenland glaciers from multi-sensor remote sensing imagery E. Zhang et al. 10.1016/j.rse.2020.112265
- GLA-STDeepLab: SAR Enhancing Glacier and Ice Shelf Front Detection Using Swin-TransDeepLab With Global–Local Attention Q. Zhu et al. 10.1109/TGRS.2023.3324404
- Extracting Glacier Calving Fronts by Deep Learning: The Benefit of Multispectral, Topographic, and Textural Input Features E. Loebel et al. 10.1109/TGRS.2022.3208454
- Out-of-the-box calving-front detection method using deep learning O. Herrmann et al. 10.5194/tc-17-4957-2023
- Mapping mountain glaciers using an improved U-Net model with cSE S. Tian et al. 10.1080/17538947.2022.2036834
- Episodic dynamic change linked to damage on the Thwaites Glacier Ice Tongue T. Surawy-Stepney et al. 10.1038/s41561-022-01097-9
- HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline K. Heidler et al. 10.1109/TGRS.2021.3064606
- Calving front monitoring at a subseasonal resolution: a deep learning application for Greenland glaciers E. Loebel et al. 10.5194/tc-18-3315-2024
- Analysis of continuous calving front retreat and the associated influencing factors of the Thwaites Glacier using high-resolution remote sensing data from 2015 to 2023 Q. Zhu et al. 10.1080/17538947.2024.2390438
- 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
- Transferable deep learning model based on the phenological matching principle for mapping crop extent S. Ge et al. 10.1016/j.jag.2021.102451
- Pixelwise Distance Regression for Glacier Calving Front Detection and Segmentation A. Davari et al. 10.1109/TGRS.2022.3158591
- A Novel Method for Automated Supraglacial Lake Mapping in Antarctica Using Sentinel-1 SAR Imagery and Deep Learning M. Dirscherl et al. 10.3390/rs13020197
- Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning C. Baumhoer et al. 10.3390/rs11212529
Discussed (preprint)
Latest update: 21 Nov 2024
Short summary
Conventionally, calving front positions have been manually delineated from remote sensing images. We design a novel method to automatically delineate the calving front positions of Jakobshavn Isbræ based on deep learning, the first of this kind for Greenland outlet glaciers. We generate high-temporal-resolution (about two measurements every month) calving fronts, demonstrating our methodology can be applied to many other tidewater glaciers through this successful case study on Jakobshavn Isbræ.
Conventionally, calving front positions have been manually delineated from remote sensing...
