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Article

Use of Very High Spatial Resolution Commercial Satellite Imagery and Deep Learning to Automatically Map Ice-Wedge Polygons across Tundra Vegetation Types

1
Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT 06269, USA
2
Woods Hole Research Center, Falmouth, MA 02540, USA
*
Author to whom correspondence should be addressed.
Received: 2 October 2020 / Revised: 1 December 2020 / Accepted: 4 December 2020 / Published: 11 December 2020
(This article belongs to the Special Issue Image Retrieval in Transfer Learning)
We developed a high-throughput mapping workflow, which centers on deep learning (DL) convolutional neural network (CNN) algorithms on high-performance distributed computing resources, to automatically characterize ice-wedge polygons (IWPs) from sub-meter resolution commercial satellite imagery. We applied a region-based CNN object instance segmentation algorithm, namely the Mask R-CNN, to automatically detect and classify IWPs in North Slope of Alaska. The central goal of our study was to systematically expound the DLCNN model interoperability across varying tundra types (sedge, tussock sedge, and non-tussock sedge) and image scene complexities to refine the understanding of opportunities and challenges for regional-scale mapping applications. We corroborated quantitative error statistics along with detailed visual inspections to gauge the IWP detection accuracies. We found promising model performances (detection accuracies: 89% to 96% and classification accuracies: 94% to 97%) for all candidate image scenes with varying tundra types. The mapping workflow discerned the IWPs by exhibiting low absolute mean relative error (AMRE) values (0.17–0.23). Results further suggest the importance of increasing the variability of training samples when practicing transfer-learning strategy to map IWPs across heterogeneous tundra cover types. Overall, our findings demonstrate the robust performances of IWPs mapping workflow in multiple tundra landscapes. View Full-Text
Keywords: permafrost; Arctic; deep learning; tundra; ice-wedge polygon; Mask R-CNN; satellite imagery permafrost; Arctic; deep learning; tundra; ice-wedge polygon; Mask R-CNN; satellite imagery
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MDPI and ACS Style

Bhuiyan, M.A.E.; Witharana, C.; Liljedahl, A.K. Use of Very High Spatial Resolution Commercial Satellite Imagery and Deep Learning to Automatically Map Ice-Wedge Polygons across Tundra Vegetation Types. J. Imaging 2020, 6, 137. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6120137

AMA Style

Bhuiyan MAE, Witharana C, Liljedahl AK. Use of Very High Spatial Resolution Commercial Satellite Imagery and Deep Learning to Automatically Map Ice-Wedge Polygons across Tundra Vegetation Types. Journal of Imaging. 2020; 6(12):137. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6120137

Chicago/Turabian Style

Bhuiyan, Md A.E.; Witharana, Chandi; Liljedahl, Anna K. 2020. "Use of Very High Spatial Resolution Commercial Satellite Imagery and Deep Learning to Automatically Map Ice-Wedge Polygons across Tundra Vegetation Types" J. Imaging 6, no. 12: 137. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6120137

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