Articles | Volume 13, issue 1
https://doi.org/10.5194/tc-13-237-2019
© 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-13-237-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Brief communication
| 
25 Jan 2019
Brief communication |
| 25 Jan 2019
| 25 Jan 2019
Brief communication: Rapid machine-learning-based extraction and measurement of ice wedge polygons in high-resolution digital elevation models
Department of Geological Sciences, The University of Texas at Austin, Austin, TX, USA
Bureau of Economic Geology, The University of Texas at Austin, Austin, TX, USA
Michael H. Young
Bureau of Economic Geology, The University of Texas at Austin, Austin, TX, USA
Adam L. Atchley
Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA
Cathy J. Wilson
Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA
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Cited
28 citations as recorded by crossref.
- Quantification of Microtopography in Natural Ecosystems Using Close-Range Remote Sensing T. Shukla et al. https://doi.org/10.3390/rs15092387
- Using deep learning to map retrogressive thaw slumps in the Beiluhe region (Tibetan Plateau) from CubeSat images L. Huang et al. https://doi.org/10.1016/j.rse.2019.111534
- A Quantitative Graph-Based Approach to Monitoring Ice-Wedge Trough Dynamics in Polygonal Permafrost Landscapes T. Rettelbach et al. https://doi.org/10.3390/rs13163098
- Artificial intelligence for geoscience: Progress, challenges, and perspectives T. Zhao et al. https://doi.org/10.1016/j.xinn.2024.100691
- Transferability of the Deep Learning Mask R-CNN Model for Automated Mapping of Ice-Wedge Polygons in High-Resolution Satellite and UAV Images W. Zhang et al. https://doi.org/10.3390/rs12071085
- InSAR estimates of excess ground ice concentrations near the permafrost table S. Zwieback et al. https://doi.org/10.1016/j.isprsjprs.2025.03.004
- Machine learning for rapid mapping of archaeological structures made of dry stones – Example of burial monuments from the Khirgisuur culture, Mongolia – F. Monna et al. https://doi.org/10.1016/j.culher.2020.01.002
- The Effect of Structure Building Small Mammals in a Shifting Arctic Landscape A. Roy & J. McLaren https://doi.org/10.1002/ece3.71523
- Formation of Low-Centered Ice-Wedge Polygons and Their Orthogonal Systems: A Review Y. Shur et al. https://doi.org/10.3390/geosciences15070249
- An Object-Based Approach for Mapping Tundra Ice-Wedge Polygon Troughs from Very High Spatial Resolution Optical Satellite Imagery C. Witharana et al. https://doi.org/10.3390/rs13040558
- Monitoring Arctic permafrost – examining the contribution of volunteered geographic information to mapping ice-wedge polygons P. Walz et al. https://doi.org/10.5194/tc-19-6355-2025
- Geometry of last glacial sorted nets from high-resolution airborne data T. Uxa et al. https://doi.org/10.1016/j.geomorph.2023.108615
- High-resolution mapping of spatial heterogeneity in ice wedge polygon geomorphology near Prudhoe Bay, Alaska C. Abolt & M. Young https://doi.org/10.1038/s41597-020-0423-9
- PixelDINO: Semi-Supervised Semantic Segmentation for Detecting Permafrost Disturbances in the Arctic K. Heidler et al. https://doi.org/10.1109/TGRS.2024.3448294
- Rapid transformation of tundra ecosystems from ice-wedge degradation M. Jorgenson et al. https://doi.org/10.1016/j.gloplacha.2022.103921
- Heterogeneity in ice-wedge permafrost degradation revealed across spatial scales K. Braun & C. Andresen https://doi.org/10.1016/j.rse.2024.114299
- Brief communication: Rapid machine-learning-based extraction and measurement of ice wedge polygons in high-resolution digital elevation models C. Abolt et al. https://doi.org/10.5194/tc-13-237-2019
- New insights into the drainage of inundated ice-wedge polygons using fundamental hydrologic principles D. Harp et al. https://doi.org/10.5194/tc-15-4005-2021
- Developing and Testing a Deep Learning Approach for Mapping Retrogressive Thaw Slumps I. Nitze et al. https://doi.org/10.3390/rs13214294
- Understanding the Effects of Optimal Combination of Spectral Bands on Deep Learning Model Predictions: A Case Study Based on Permafrost Tundra Landform Mapping Using High Resolution Multispectral Satellite Imagery M. Bhuiyan et al. https://doi.org/10.3390/jimaging6090097
- Multiscale Object-Based Classification and Feature Extraction along Arctic Coasts A. Clark et al. https://doi.org/10.3390/rs14132982
- Large-scale mapping of solifluction terraces in the southeastern Tibetan Plateau using high-resolution satellite images and deep learning R. Huang et al. https://doi.org/10.1016/j.geomorph.2023.108626
- The Predictive Skill of a Remote Sensing-Based Machine Learning Model for Ice Wedge and Visible Ground Ice Identification in Western Arctic Canada Q. Chang et al. https://doi.org/10.3390/rs17071245
- Understanding the synergies of deep learning and data fusion of multispectral and panchromatic high resolution commercial satellite imagery for automated ice-wedge polygon detection C. Witharana et al. https://doi.org/10.1016/j.isprsjprs.2020.10.010
- Fully automated snow depth measurements from time-lapse images applying a convolutional neural network M. Kopp et al. https://doi.org/10.1016/j.scitotenv.2019.134213
- Accuracy, Efficiency, and Transferability of a Deep Learning Model for Mapping Retrogressive Thaw Slumps across the Canadian Arctic L. Huang et al. https://doi.org/10.3390/rs14122747
- Squeezing Data from a Rock: Machine Learning for Martian Science T. Nagle-McNaughton et al. https://doi.org/10.3390/geosciences12060248
- Subsurface robotic exploration for geomorphology, astrobiology and mining during MINAR6 campaign, Boulby Mine, UK: part II (Results and Discussion) T. Mathanlal et al. https://doi.org/10.1017/S1473550420000385
28 citations as recorded by crossref.
- Quantification of Microtopography in Natural Ecosystems Using Close-Range Remote Sensing T. Shukla et al. https://doi.org/10.3390/rs15092387
- Using deep learning to map retrogressive thaw slumps in the Beiluhe region (Tibetan Plateau) from CubeSat images L. Huang et al. https://doi.org/10.1016/j.rse.2019.111534
- A Quantitative Graph-Based Approach to Monitoring Ice-Wedge Trough Dynamics in Polygonal Permafrost Landscapes T. Rettelbach et al. https://doi.org/10.3390/rs13163098
- Artificial intelligence for geoscience: Progress, challenges, and perspectives T. Zhao et al. https://doi.org/10.1016/j.xinn.2024.100691
- Transferability of the Deep Learning Mask R-CNN Model for Automated Mapping of Ice-Wedge Polygons in High-Resolution Satellite and UAV Images W. Zhang et al. https://doi.org/10.3390/rs12071085
- InSAR estimates of excess ground ice concentrations near the permafrost table S. Zwieback et al. https://doi.org/10.1016/j.isprsjprs.2025.03.004
- Machine learning for rapid mapping of archaeological structures made of dry stones – Example of burial monuments from the Khirgisuur culture, Mongolia – F. Monna et al. https://doi.org/10.1016/j.culher.2020.01.002
- The Effect of Structure Building Small Mammals in a Shifting Arctic Landscape A. Roy & J. McLaren https://doi.org/10.1002/ece3.71523
- Formation of Low-Centered Ice-Wedge Polygons and Their Orthogonal Systems: A Review Y. Shur et al. https://doi.org/10.3390/geosciences15070249
- An Object-Based Approach for Mapping Tundra Ice-Wedge Polygon Troughs from Very High Spatial Resolution Optical Satellite Imagery C. Witharana et al. https://doi.org/10.3390/rs13040558
- Monitoring Arctic permafrost – examining the contribution of volunteered geographic information to mapping ice-wedge polygons P. Walz et al. https://doi.org/10.5194/tc-19-6355-2025
- Geometry of last glacial sorted nets from high-resolution airborne data T. Uxa et al. https://doi.org/10.1016/j.geomorph.2023.108615
- High-resolution mapping of spatial heterogeneity in ice wedge polygon geomorphology near Prudhoe Bay, Alaska C. Abolt & M. Young https://doi.org/10.1038/s41597-020-0423-9
- PixelDINO: Semi-Supervised Semantic Segmentation for Detecting Permafrost Disturbances in the Arctic K. Heidler et al. https://doi.org/10.1109/TGRS.2024.3448294
- Rapid transformation of tundra ecosystems from ice-wedge degradation M. Jorgenson et al. https://doi.org/10.1016/j.gloplacha.2022.103921
- Heterogeneity in ice-wedge permafrost degradation revealed across spatial scales K. Braun & C. Andresen https://doi.org/10.1016/j.rse.2024.114299
- Brief communication: Rapid machine-learning-based extraction and measurement of ice wedge polygons in high-resolution digital elevation models C. Abolt et al. https://doi.org/10.5194/tc-13-237-2019
- New insights into the drainage of inundated ice-wedge polygons using fundamental hydrologic principles D. Harp et al. https://doi.org/10.5194/tc-15-4005-2021
- Developing and Testing a Deep Learning Approach for Mapping Retrogressive Thaw Slumps I. Nitze et al. https://doi.org/10.3390/rs13214294
- Understanding the Effects of Optimal Combination of Spectral Bands on Deep Learning Model Predictions: A Case Study Based on Permafrost Tundra Landform Mapping Using High Resolution Multispectral Satellite Imagery M. Bhuiyan et al. https://doi.org/10.3390/jimaging6090097
- Multiscale Object-Based Classification and Feature Extraction along Arctic Coasts A. Clark et al. https://doi.org/10.3390/rs14132982
- Large-scale mapping of solifluction terraces in the southeastern Tibetan Plateau using high-resolution satellite images and deep learning R. Huang et al. https://doi.org/10.1016/j.geomorph.2023.108626
- The Predictive Skill of a Remote Sensing-Based Machine Learning Model for Ice Wedge and Visible Ground Ice Identification in Western Arctic Canada Q. Chang et al. https://doi.org/10.3390/rs17071245
- Understanding the synergies of deep learning and data fusion of multispectral and panchromatic high resolution commercial satellite imagery for automated ice-wedge polygon detection C. Witharana et al. https://doi.org/10.1016/j.isprsjprs.2020.10.010
- Fully automated snow depth measurements from time-lapse images applying a convolutional neural network M. Kopp et al. https://doi.org/10.1016/j.scitotenv.2019.134213
- Accuracy, Efficiency, and Transferability of a Deep Learning Model for Mapping Retrogressive Thaw Slumps across the Canadian Arctic L. Huang et al. https://doi.org/10.3390/rs14122747
- Squeezing Data from a Rock: Machine Learning for Martian Science T. Nagle-McNaughton et al. https://doi.org/10.3390/geosciences12060248
- Subsurface robotic exploration for geomorphology, astrobiology and mining during MINAR6 campaign, Boulby Mine, UK: part II (Results and Discussion) T. Mathanlal et al. https://doi.org/10.1017/S1473550420000385
Saved (final revised paper)
Latest update: 16 Sep 2026
Short summary
We present a workflow that uses a machine-learning algorithm known as a convolutional neural network (CNN) to rapidly delineate ice wedge polygons in high-resolution topographic datasets. Our workflow permits thorough assessments of polygonal microtopography at the kilometer scale or greater, which can improve understanding of landscape hydrology and carbon budgets. We demonstrate that a single CNN can be trained to delineate polygons with high accuracy in diverse tundra settings.
We present a workflow that uses a machine-learning algorithm known as a convolutional neural...
