A Parallel Workflow for Polar Sea-Ice Classification using Auto-labeling of Sentinel-2 Imagery ...
The observation of the advancing and retreating pattern of polar sea ice cover stands as a vital indicator of global warming. This research aims to develop a robust, effective, and scalable system for classifying polar sea ice as thick/snow-covered, young/thin, or open water using Sentinel-2 (S2) im...
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Online Access: | https://dx.doi.org/10.48550/arxiv.2403.13135 https://arxiv.org/abs/2403.13135 |
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ftdatacite:10.48550/arxiv.2403.13135 2024-04-28T08:37:46+00:00 A Parallel Workflow for Polar Sea-Ice Classification using Auto-labeling of Sentinel-2 Imagery ... Iqrah, Jurdana Masuma Wang, Wei Xie, Hongjie Prasad, Sushil 2024 https://dx.doi.org/10.48550/arxiv.2403.13135 https://arxiv.org/abs/2403.13135 unknown arXiv Creative Commons Attribution Non Commercial Share Alike 4.0 International https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode cc-by-nc-sa-4.0 Computer Vision and Pattern Recognition cs.CV Distributed, Parallel, and Cluster Computing cs.DC Machine Learning cs.LG FOS Computer and information sciences article Article Preprint CreativeWork 2024 ftdatacite https://doi.org/10.48550/arxiv.2403.13135 2024-04-02T11:50:35Z The observation of the advancing and retreating pattern of polar sea ice cover stands as a vital indicator of global warming. This research aims to develop a robust, effective, and scalable system for classifying polar sea ice as thick/snow-covered, young/thin, or open water using Sentinel-2 (S2) images. Since the S2 satellite is actively capturing high-resolution imagery over the earth's surface, there are lots of images that need to be classified. One major obstacle is the absence of labeled S2 training data (images) to act as the ground truth. We demonstrate a scalable and accurate method for segmenting and automatically labeling S2 images using carefully determined color thresholds. We employ a parallel workflow using PySpark to scale and achieve 9-fold data loading and 16-fold map-reduce speedup on auto-labeling S2 images based on thin cloud and shadow-filtered color-based segmentation to generate label data. The auto-labeled data generated from this process are then employed to train a U-Net machine ... : Accepted in the 25th IEEE International Workshop on Parallel and Distributed Scientific and Engineering Computing (PDSEC 2024), May 2024. arXiv admin note: substantial text overlap with arXiv:2303.12719 ... Article in Journal/Newspaper Sea ice DataCite Metadata Store (German National Library of Science and Technology) |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
Computer Vision and Pattern Recognition cs.CV Distributed, Parallel, and Cluster Computing cs.DC Machine Learning cs.LG FOS Computer and information sciences |
spellingShingle |
Computer Vision and Pattern Recognition cs.CV Distributed, Parallel, and Cluster Computing cs.DC Machine Learning cs.LG FOS Computer and information sciences Iqrah, Jurdana Masuma Wang, Wei Xie, Hongjie Prasad, Sushil A Parallel Workflow for Polar Sea-Ice Classification using Auto-labeling of Sentinel-2 Imagery ... |
topic_facet |
Computer Vision and Pattern Recognition cs.CV Distributed, Parallel, and Cluster Computing cs.DC Machine Learning cs.LG FOS Computer and information sciences |
description |
The observation of the advancing and retreating pattern of polar sea ice cover stands as a vital indicator of global warming. This research aims to develop a robust, effective, and scalable system for classifying polar sea ice as thick/snow-covered, young/thin, or open water using Sentinel-2 (S2) images. Since the S2 satellite is actively capturing high-resolution imagery over the earth's surface, there are lots of images that need to be classified. One major obstacle is the absence of labeled S2 training data (images) to act as the ground truth. We demonstrate a scalable and accurate method for segmenting and automatically labeling S2 images using carefully determined color thresholds. We employ a parallel workflow using PySpark to scale and achieve 9-fold data loading and 16-fold map-reduce speedup on auto-labeling S2 images based on thin cloud and shadow-filtered color-based segmentation to generate label data. The auto-labeled data generated from this process are then employed to train a U-Net machine ... : Accepted in the 25th IEEE International Workshop on Parallel and Distributed Scientific and Engineering Computing (PDSEC 2024), May 2024. arXiv admin note: substantial text overlap with arXiv:2303.12719 ... |
format |
Article in Journal/Newspaper |
author |
Iqrah, Jurdana Masuma Wang, Wei Xie, Hongjie Prasad, Sushil |
author_facet |
Iqrah, Jurdana Masuma Wang, Wei Xie, Hongjie Prasad, Sushil |
author_sort |
Iqrah, Jurdana Masuma |
title |
A Parallel Workflow for Polar Sea-Ice Classification using Auto-labeling of Sentinel-2 Imagery ... |
title_short |
A Parallel Workflow for Polar Sea-Ice Classification using Auto-labeling of Sentinel-2 Imagery ... |
title_full |
A Parallel Workflow for Polar Sea-Ice Classification using Auto-labeling of Sentinel-2 Imagery ... |
title_fullStr |
A Parallel Workflow for Polar Sea-Ice Classification using Auto-labeling of Sentinel-2 Imagery ... |
title_full_unstemmed |
A Parallel Workflow for Polar Sea-Ice Classification using Auto-labeling of Sentinel-2 Imagery ... |
title_sort |
parallel workflow for polar sea-ice classification using auto-labeling of sentinel-2 imagery ... |
publisher |
arXiv |
publishDate |
2024 |
url |
https://dx.doi.org/10.48550/arxiv.2403.13135 https://arxiv.org/abs/2403.13135 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_rights |
Creative Commons Attribution Non Commercial Share Alike 4.0 International https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode cc-by-nc-sa-4.0 |
op_doi |
https://doi.org/10.48550/arxiv.2403.13135 |
_version_ |
1797569088333021184 |