Semi-Automated Semantic Segmentation of Arctic Shorelines Using Very High-Resolution Airborne Imagery, Spectral Indices and Weakly Supervised Machine Learning Approaches
Precise coastal shoreline mapping is essential for monitoring changes in erosion rates, surface hydrology, and ecosystem structure and function. Monitoring water bodies in the Arctic National Wildlife Refuge (ANWR) is of high importance, especially considering the potential for oil and natural gas e...
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ftdoajarticles:oai:doaj.org/article:72db7bdf46df409681f6ce7218ef9a4b 2023-05-15T14:50:55+02:00 Semi-Automated Semantic Segmentation of Arctic Shorelines Using Very High-Resolution Airborne Imagery, Spectral Indices and Weakly Supervised Machine Learning Approaches Bibek Aryal Stephen M. Escarzaga Sergio A. Vargas Zesati Miguel Velez-Reyes Olac Fuentes Craig Tweedie 2021-11-01T00:00:00Z https://doi.org/10.3390/rs13224572 https://doaj.org/article/72db7bdf46df409681f6ce7218ef9a4b EN eng MDPI AG https://www.mdpi.com/2072-4292/13/22/4572 https://doaj.org/toc/2072-4292 doi:10.3390/rs13224572 2072-4292 https://doaj.org/article/72db7bdf46df409681f6ce7218ef9a4b Remote Sensing, Vol 13, Iss 4572, p 4572 (2021) land water segmentation remote sensing deep learning sparse labels Science Q article 2021 ftdoajarticles https://doi.org/10.3390/rs13224572 2022-12-30T20:32:01Z Precise coastal shoreline mapping is essential for monitoring changes in erosion rates, surface hydrology, and ecosystem structure and function. Monitoring water bodies in the Arctic National Wildlife Refuge (ANWR) is of high importance, especially considering the potential for oil and natural gas exploration in the region. In this work, we propose a modified variant of the Deep Neural Network based U-Net Architecture for the automated mapping of 4 Band Orthorectified NOAA Airborne Imagery using sparsely labeled training data and compare it to the performance of traditional Machine Learning (ML) based approaches—namely, random forest, xgboost—and spectral water indices—Normalized Difference Water Index (NDWI), and Normalized Difference Surface Water Index (NDSWI)—to support shoreline mapping of Arctic coastlines. We conclude that it is possible to modify the U-Net model to accept sparse labels as input and the results are comparable to other ML methods (an Intersection-over-Union (IoU) of 94.86% using U-Net vs. an IoU of 95.05% using the best performing method). Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Remote Sensing 13 22 4572 |
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Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
land water segmentation remote sensing deep learning sparse labels Science Q |
spellingShingle |
land water segmentation remote sensing deep learning sparse labels Science Q Bibek Aryal Stephen M. Escarzaga Sergio A. Vargas Zesati Miguel Velez-Reyes Olac Fuentes Craig Tweedie Semi-Automated Semantic Segmentation of Arctic Shorelines Using Very High-Resolution Airborne Imagery, Spectral Indices and Weakly Supervised Machine Learning Approaches |
topic_facet |
land water segmentation remote sensing deep learning sparse labels Science Q |
description |
Precise coastal shoreline mapping is essential for monitoring changes in erosion rates, surface hydrology, and ecosystem structure and function. Monitoring water bodies in the Arctic National Wildlife Refuge (ANWR) is of high importance, especially considering the potential for oil and natural gas exploration in the region. In this work, we propose a modified variant of the Deep Neural Network based U-Net Architecture for the automated mapping of 4 Band Orthorectified NOAA Airborne Imagery using sparsely labeled training data and compare it to the performance of traditional Machine Learning (ML) based approaches—namely, random forest, xgboost—and spectral water indices—Normalized Difference Water Index (NDWI), and Normalized Difference Surface Water Index (NDSWI)—to support shoreline mapping of Arctic coastlines. We conclude that it is possible to modify the U-Net model to accept sparse labels as input and the results are comparable to other ML methods (an Intersection-over-Union (IoU) of 94.86% using U-Net vs. an IoU of 95.05% using the best performing method). |
format |
Article in Journal/Newspaper |
author |
Bibek Aryal Stephen M. Escarzaga Sergio A. Vargas Zesati Miguel Velez-Reyes Olac Fuentes Craig Tweedie |
author_facet |
Bibek Aryal Stephen M. Escarzaga Sergio A. Vargas Zesati Miguel Velez-Reyes Olac Fuentes Craig Tweedie |
author_sort |
Bibek Aryal |
title |
Semi-Automated Semantic Segmentation of Arctic Shorelines Using Very High-Resolution Airborne Imagery, Spectral Indices and Weakly Supervised Machine Learning Approaches |
title_short |
Semi-Automated Semantic Segmentation of Arctic Shorelines Using Very High-Resolution Airborne Imagery, Spectral Indices and Weakly Supervised Machine Learning Approaches |
title_full |
Semi-Automated Semantic Segmentation of Arctic Shorelines Using Very High-Resolution Airborne Imagery, Spectral Indices and Weakly Supervised Machine Learning Approaches |
title_fullStr |
Semi-Automated Semantic Segmentation of Arctic Shorelines Using Very High-Resolution Airborne Imagery, Spectral Indices and Weakly Supervised Machine Learning Approaches |
title_full_unstemmed |
Semi-Automated Semantic Segmentation of Arctic Shorelines Using Very High-Resolution Airborne Imagery, Spectral Indices and Weakly Supervised Machine Learning Approaches |
title_sort |
semi-automated semantic segmentation of arctic shorelines using very high-resolution airborne imagery, spectral indices and weakly supervised machine learning approaches |
publisher |
MDPI AG |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13224572 https://doaj.org/article/72db7bdf46df409681f6ce7218ef9a4b |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Remote Sensing, Vol 13, Iss 4572, p 4572 (2021) |
op_relation |
https://www.mdpi.com/2072-4292/13/22/4572 https://doaj.org/toc/2072-4292 doi:10.3390/rs13224572 2072-4292 https://doaj.org/article/72db7bdf46df409681f6ce7218ef9a4b |
op_doi |
https://doi.org/10.3390/rs13224572 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
22 |
container_start_page |
4572 |
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1766321973924724736 |