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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/tc-2018-167
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-2018-167
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Brief communication 11 Sep 2018

Brief communication | 11 Sep 2018

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal The Cryosphere (TC).

Brief communication: Rapid machine learning-based extraction and measurement of ice wedge polygons in airborne lidar data

Charles J. Abolt1,2, Michael H. Young2, Adam A. Atchley3, and Cathy J. Wilson3 Charles J. Abolt et al.
  • 1Department of Geological Sciences, The University of Texas at Austin, Austin, TX, USA
  • 2Bureau of Economic Geology, The University of Texas at Austin, Austin, TX USA
  • 3Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA

Abstract. We present a workflow for rapid delineation and microtopographic characterization of ice wedge polygons within high-resolution digital elevation models. The workflow, which is extensible to other forms of remotely sensed imagery, incorporates a convolutional neural network to detect pixels representing troughs. A watershed transformation is then used to segment imagery into discrete polygons. Regions of non-polygonal terrain are partitioned out using a simple post-processing procedure. Results from training and validation sites at Barrow and Prudhoe Bay, Alaska demonstrate robust performance in diverse tundra landscapes. The methodology permits fast, spatially extensive measurements of polygonal microtopography and trough network geometry.

Charles J. Abolt et al.
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Status: final response (author comments only)
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Charles J. Abolt et al.
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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 from high resolution topographic data. 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 from diverse tundra landscapes.
We present a workflow that uses a machine-learning algorithm known as a convolutional neural...
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