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

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 a...

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Bibliographic Details
Published in:Journal of Imaging
Main Authors: Md Abul Ehsan Bhuiyan, Chandi Witharana, Anna K. Liljedahl
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2020
Subjects:
permafrost
Arctic
deep learning
tundra
ice-wedge polygon
Mask R-CNN
Photography
TR1-1050
Computer applications to medicine. Medical informatics
R858-859.7
Electronic computers. Computer science
QA75.5-76.95
Ice
north slope
Tundra
wedge*
Alaska
Online Access:https://doi.org/10.3390/jimaging6120137
https://doaj.org/article/83668c1d94ca4a5f817210c046c01570
Description
Summary: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.

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