Tracing Englacial Layers in Radargram via Semi-supervised Method: A Preliminary Result
The melting of ice sheets significantly contributes to sea level rise, highlighting the crucial need to comprehend the structure of ice for climate benefits. The stratigraphy of ice sheets revealed through ice layer radargrams gives us a window into historical depth-age correlations and accumulation...
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ftjaaai:oai:ojs.aaai.org:article/27653 2024-02-11T10:04:24+01:00 Tracing Englacial Layers in Radargram via Semi-supervised Method: A Preliminary Result Jebeli, Atefeh Tama, Bayu Adhi Purushotham, Sanjay Janeja, Vandana P. 2024-01-22 application/pdf https://ojs.aaai.org/index.php/AAAI-SS/article/view/27653 https://doi.org/10.1609/aaaiss.v2i1.27653 eng eng AAAI Press https://ojs.aaai.org/index.php/AAAI-SS/article/view/27653/27426 https://ojs.aaai.org/index.php/AAAI-SS/article/view/27653 doi:10.1609/aaaiss.v2i1.27653 Copyright (c) 2023 Association for the Advancement of Artificial Intelligence Proceedings of the AAAI Symposium Series; Vol. 2 No. 1: Proceedings of the 2023 AAAI Fall Symposia; 85-88 2994-4317 Deep Learning Ice Layer Annotation Sea Level Rise Semi-supervised Learning Radargram info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article 2024 ftjaaai https://doi.org/10.1609/aaaiss.v2i1.27653 2024-01-27T23:49:56Z The melting of ice sheets significantly contributes to sea level rise, highlighting the crucial need to comprehend the structure of ice for climate benefits. The stratigraphy of ice sheets revealed through ice layer radargrams gives us a window into historical depth-age correlations and accumulation rates. Harnessing this knowledge is not only crucial for interpreting both past and present ice dynamics, especially concerning the Greenland ice sheet, but also for making informed decisions to mitigate the impacts of climate change. Ice layer tracing is prevalently conducted using manual or semi-automatic approaches, requiring significant time and expertise. This study aims to address the need for efficient and precise tracing methods in a two-step process. This is achieved by utilizing an unsupervised annotation method (i.e., ARESELP) to train deep learning models, thereby reducing the need for extensive and time-consuming manual annotations. Four prominent deep learning-based segmentation techniques, namely U-Net, U-Net+VGG19, U-Net+Inception, and Attention U-Net, are benchmarked. Additionally, various thresholding methods such as binary, Otsu, and CLAHE have been explored to achieve optimal enhancement for the true label annotation images. Our preliminary experiments indicate that the combination of attention U-Net with specific processing techniques yields the best performance in terms of the binary IoU metric. Article in Journal/Newspaper Greenland Ice Sheet AAAI Publications (Association for the Advancement of Artificial Intelligence) Greenland Proceedings of the AAAI Symposium Series 2 1 85 88 |
institution |
Open Polar |
collection |
AAAI Publications (Association for the Advancement of Artificial Intelligence) |
op_collection_id |
ftjaaai |
language |
English |
topic |
Deep Learning Ice Layer Annotation Sea Level Rise Semi-supervised Learning Radargram |
spellingShingle |
Deep Learning Ice Layer Annotation Sea Level Rise Semi-supervised Learning Radargram Jebeli, Atefeh Tama, Bayu Adhi Purushotham, Sanjay Janeja, Vandana P. Tracing Englacial Layers in Radargram via Semi-supervised Method: A Preliminary Result |
topic_facet |
Deep Learning Ice Layer Annotation Sea Level Rise Semi-supervised Learning Radargram |
description |
The melting of ice sheets significantly contributes to sea level rise, highlighting the crucial need to comprehend the structure of ice for climate benefits. The stratigraphy of ice sheets revealed through ice layer radargrams gives us a window into historical depth-age correlations and accumulation rates. Harnessing this knowledge is not only crucial for interpreting both past and present ice dynamics, especially concerning the Greenland ice sheet, but also for making informed decisions to mitigate the impacts of climate change. Ice layer tracing is prevalently conducted using manual or semi-automatic approaches, requiring significant time and expertise. This study aims to address the need for efficient and precise tracing methods in a two-step process. This is achieved by utilizing an unsupervised annotation method (i.e., ARESELP) to train deep learning models, thereby reducing the need for extensive and time-consuming manual annotations. Four prominent deep learning-based segmentation techniques, namely U-Net, U-Net+VGG19, U-Net+Inception, and Attention U-Net, are benchmarked. Additionally, various thresholding methods such as binary, Otsu, and CLAHE have been explored to achieve optimal enhancement for the true label annotation images. Our preliminary experiments indicate that the combination of attention U-Net with specific processing techniques yields the best performance in terms of the binary IoU metric. |
format |
Article in Journal/Newspaper |
author |
Jebeli, Atefeh Tama, Bayu Adhi Purushotham, Sanjay Janeja, Vandana P. |
author_facet |
Jebeli, Atefeh Tama, Bayu Adhi Purushotham, Sanjay Janeja, Vandana P. |
author_sort |
Jebeli, Atefeh |
title |
Tracing Englacial Layers in Radargram via Semi-supervised Method: A Preliminary Result |
title_short |
Tracing Englacial Layers in Radargram via Semi-supervised Method: A Preliminary Result |
title_full |
Tracing Englacial Layers in Radargram via Semi-supervised Method: A Preliminary Result |
title_fullStr |
Tracing Englacial Layers in Radargram via Semi-supervised Method: A Preliminary Result |
title_full_unstemmed |
Tracing Englacial Layers in Radargram via Semi-supervised Method: A Preliminary Result |
title_sort |
tracing englacial layers in radargram via semi-supervised method: a preliminary result |
publisher |
AAAI Press |
publishDate |
2024 |
url |
https://ojs.aaai.org/index.php/AAAI-SS/article/view/27653 https://doi.org/10.1609/aaaiss.v2i1.27653 |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland Ice Sheet |
genre_facet |
Greenland Ice Sheet |
op_source |
Proceedings of the AAAI Symposium Series; Vol. 2 No. 1: Proceedings of the 2023 AAAI Fall Symposia; 85-88 2994-4317 |
op_relation |
https://ojs.aaai.org/index.php/AAAI-SS/article/view/27653/27426 https://ojs.aaai.org/index.php/AAAI-SS/article/view/27653 doi:10.1609/aaaiss.v2i1.27653 |
op_rights |
Copyright (c) 2023 Association for the Advancement of Artificial Intelligence |
op_doi |
https://doi.org/10.1609/aaaiss.v2i1.27653 |
container_title |
Proceedings of the AAAI Symposium Series |
container_volume |
2 |
container_issue |
1 |
container_start_page |
85 |
op_container_end_page |
88 |
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1790600998402654208 |