Articles | Volume 13, issue 1
The Cryosphere, 13, 237–245, 2019
https://doi.org/10.5194/tc-13-237-2019
The Cryosphere, 13, 237–245, 2019
https://doi.org/10.5194/tc-13-237-2019

Brief communication 25 Jan 2019

Brief communication | 25 Jan 2019

Brief communication: Rapid machine-learning-based extraction and measurement of ice wedge polygons in high-resolution digital elevation models

Charles J. Abolt et al.

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Cited articles

Abolt, C. J., Young, M. H., Atchley, A. L., and Brown, C. J.: CNN-watershed: A machine-learning based tool for delineation and measurement of ice wedge polygons in high-resolution digital elevation models, Zenodo repository, https://doi.org/10.5821/zenodo.2537167, 2018. 
Ciresan, D., Giusti, A., Gambardella, L. M. and Schmidhuber, J.: Deep neural networks segment neuronal membranes in electron microscopy images, in: Advances in Neural Information Processing Systems 25, edited by: Pereira, F., Burges, C. J. C., Bottou, L., and Weinberger, Q., Curran Associates, Inc., 2843–2851, 2012. 
He, K., Gkioxari, G., Dollar, P., and Girshick, R.: Mask R-CNN, in: Proceedings of the 2017 IEEE International Conference on Computer Vision, IEEE, Piscataway, NJ, USA, 2017. 
Jorgenson, M. T., Shur, Y. L., and Pullman, E. R.: Abrupt increase in permafrost degradation in Arctic Alaska, Geophys. Res. Lett., 33, L02503, https://doi.org/10.1029/2005GL024960, 2006. 
Jorgenson, M. T., Kanevskiy, M., Shur, Y., Moskalenko, N., Brown, D. R. N., Wickland, K., Striegl, R. and Koch, J.: Role of ground ice dynamics and ecological feedbacks in recent ice wedge degradation and stabilization, J. Geophys. Res.-Earth Surf., 120, 2280–2297, https://doi.org/10.1002/2015JF003602, 2015. 
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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.