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Rapid global warming is catalyzing widespread permafrost degradation in the Arctic, leading to destructive land-surface subsidence that destabilizes and deforms the ground. Consequently, human-built infrastructure constructed upon permafrost is currently at major risk of structural failure. Risk assessment frameworks that attempt to study this issue assume that precise information on the location and extent of infrastructure is known. However, complete, high-quality, uniform geospatial datasets of built infrastructure that are readily available for such scientific studies are lacking. While imagery-enabled mapping can fill this knowledge gap, the small size of individual structures and vast geographical extent of the Arctic necessitate large volumes of very high spatial resolution remote sensing imagery. Transforming this ‘big’ imagery data into ‘science-ready’ information demands highly automated image analysis pipelines driven by advanced computer vision algorithms. Despite this, previous fine resolution studies have been limited to manual digitization of features on locally confined scales. Therefore, this exploratory study serves as the first investigation into fully automated analysis of sub-meter spatial resolution satellite imagery for automated detection of Arctic built infrastructure. We tasked the U-Net, a deep learning-based semantic segmentation model, with classifying different infrastructure types (residential, commercial, public, and industrial buildings, as well as roads) from commercial satellite imagery of Utqiagvik and Prudhoe Bay, Alaska. We also conducted a systematic experiment to understand how image augmentation can impact model performance when labeled training data is limited. When optimal augmentation methods were applied, the U-Net achieved an average F1 score of 0.83. Overall, our experimental findings show that the U-Net-based workflow is a promising method for automated Arctic built infrastructure detection that, combined with existing optimized workflows, such as MAPLE, could be expanded to map a multitude of infrastructure types spanning the pan-Arctic.
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Locations of beaded streams in the Pan Arctic, 2023 (Harlan et al 2023 Remote Sensing of Environment)
Here we observe and predict the location of beaded stream catchments throughout the pan-Arctic domain by combining the location of known beaded streams with recent advances in computer vision and high-resolution (3 meter (m)) satellite imagery. Specifically, we use the location of known existing beaded streams to classify potential river catchments as beaded or non-beaded, then download high resolution imagery across those regions, and use the latest You-Only-Look-Once (YOLO) object detection algorithm to identify beaded streams throughout the pan-Arctic, estimating 138,500 ± 43,700 beaded catchments globally, occurring in an estimated one third of all pan-Arctic catchments. In the largest dataset of beaded streams to date (Arp et al., 2015), only 375 catchments that contain beaded streams were identified, thus our estimate significantly expands our current understanding of the location and prevalence of Arctic beaded streams. Data is accessible through the alternate identifier link on Zenodo: https://doi.org/10.5281/zenodo.7223256
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- Award ID(s):
- 1748653
- PAR ID:
- 10516323
- Publisher / Repository:
- NSF Arctic Data Center
- Date Published:
- Subject(s) / Keyword(s):
- Beaded streams Planet Rivers Geomorphology
- Format(s):
- Medium: X Other: text/xml
- Sponsoring Org:
- National Science Foundation
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