Convolutional Neural Networks for Automated Built Infrastructure Detection in the Arctic Using Sub-Meter Spatial Resolution Satellite Imagery
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 ass...
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Online Access: | https://doi.org/10.3390/rs14112719 https://doaj.org/article/e24a4f98f9b04576ba5465e0ebb8a3ce |
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ftdoajarticles:oai:doaj.org/article:e24a4f98f9b04576ba5465e0ebb8a3ce 2023-05-15T14:50:08+02:00 Convolutional Neural Networks for Automated Built Infrastructure Detection in the Arctic Using Sub-Meter Spatial Resolution Satellite Imagery Elias Manos Chandi Witharana Mahendra Rajitha Udawalpola Amit Hasan Anna K. Liljedahl 2022-06-01T00:00:00Z https://doi.org/10.3390/rs14112719 https://doaj.org/article/e24a4f98f9b04576ba5465e0ebb8a3ce EN eng MDPI AG https://www.mdpi.com/2072-4292/14/11/2719 https://doaj.org/toc/2072-4292 doi:10.3390/rs14112719 2072-4292 https://doaj.org/article/e24a4f98f9b04576ba5465e0ebb8a3ce Remote Sensing, Vol 14, Iss 2719, p 2719 (2022) deep learning artificial intelligence semantic segmentation U-Net VHSR imagery building extraction Science Q article 2022 ftdoajarticles https://doi.org/10.3390/rs14112719 2022-12-31T01:56:48Z 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 ... Article in Journal/Newspaper Arctic Global warming permafrost Prudhoe Bay Alaska Directory of Open Access Journals: DOAJ Articles Arctic Remote Sensing 14 11 2719 |
institution |
Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
deep learning artificial intelligence semantic segmentation U-Net VHSR imagery building extraction Science Q |
spellingShingle |
deep learning artificial intelligence semantic segmentation U-Net VHSR imagery building extraction Science Q Elias Manos Chandi Witharana Mahendra Rajitha Udawalpola Amit Hasan Anna K. Liljedahl Convolutional Neural Networks for Automated Built Infrastructure Detection in the Arctic Using Sub-Meter Spatial Resolution Satellite Imagery |
topic_facet |
deep learning artificial intelligence semantic segmentation U-Net VHSR imagery building extraction Science Q |
description |
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 ... |
format |
Article in Journal/Newspaper |
author |
Elias Manos Chandi Witharana Mahendra Rajitha Udawalpola Amit Hasan Anna K. Liljedahl |
author_facet |
Elias Manos Chandi Witharana Mahendra Rajitha Udawalpola Amit Hasan Anna K. Liljedahl |
author_sort |
Elias Manos |
title |
Convolutional Neural Networks for Automated Built Infrastructure Detection in the Arctic Using Sub-Meter Spatial Resolution Satellite Imagery |
title_short |
Convolutional Neural Networks for Automated Built Infrastructure Detection in the Arctic Using Sub-Meter Spatial Resolution Satellite Imagery |
title_full |
Convolutional Neural Networks for Automated Built Infrastructure Detection in the Arctic Using Sub-Meter Spatial Resolution Satellite Imagery |
title_fullStr |
Convolutional Neural Networks for Automated Built Infrastructure Detection in the Arctic Using Sub-Meter Spatial Resolution Satellite Imagery |
title_full_unstemmed |
Convolutional Neural Networks for Automated Built Infrastructure Detection in the Arctic Using Sub-Meter Spatial Resolution Satellite Imagery |
title_sort |
convolutional neural networks for automated built infrastructure detection in the arctic using sub-meter spatial resolution satellite imagery |
publisher |
MDPI AG |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14112719 https://doaj.org/article/e24a4f98f9b04576ba5465e0ebb8a3ce |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Global warming permafrost Prudhoe Bay Alaska |
genre_facet |
Arctic Global warming permafrost Prudhoe Bay Alaska |
op_source |
Remote Sensing, Vol 14, Iss 2719, p 2719 (2022) |
op_relation |
https://www.mdpi.com/2072-4292/14/11/2719 https://doaj.org/toc/2072-4292 doi:10.3390/rs14112719 2072-4292 https://doaj.org/article/e24a4f98f9b04576ba5465e0ebb8a3ce |
op_doi |
https://doi.org/10.3390/rs14112719 |
container_title |
Remote Sensing |
container_volume |
14 |
container_issue |
11 |
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2719 |
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1766321203801227264 |