Refining Ice Layer Tracking through Wavelet combined Neural Networks (Papers Track) ...
Tackling Climate Change with Machine Learning Workshop at ICML 2021. ... : Rise in global temperatures is resulting in polar ice caps to melt away, which can lead to drastic sea level rise and coastal floods. Accurate calculation of the ice cap reduction is necessary in order to project its climatic...
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Online Access: | https://dx.doi.org/10.13016/m2p5lq-u1h8 https://mdsoar.org/handle/11603/22312 |
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ftdatacite:10.13016/m2p5lq-u1h8 2023-08-27T04:09:57+02:00 Refining Ice Layer Tracking through Wavelet combined Neural Networks (Papers Track) ... Varshney, Debvrat Yari, Masoud Chowdhury, Tashnim Rahnemoonfar, Maryam 2021 https://dx.doi.org/10.13016/m2p5lq-u1h8 https://mdsoar.org/handle/11603/22312 en eng Maryland Shared Open Access Repository This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. global temperatures convolutional neural networks ice cap reduction MovingImage Collection article 2021 ftdatacite https://doi.org/10.13016/m2p5lq-u1h8 2023-08-07T14:24:23Z Tackling Climate Change with Machine Learning Workshop at ICML 2021. ... : Rise in global temperatures is resulting in polar ice caps to melt away, which can lead to drastic sea level rise and coastal floods. Accurate calculation of the ice cap reduction is necessary in order to project its climatic impact. Ice sheets are monitored through Snow Radar sensors which give noisy profiles of subsurface ice layers. The sensors take snapshots of the entire ice sheet regularly, and thus result in large datasets. In this work, we use convolutional neural networks (CNNs) for their property of feature extraction and generalizability on large datasets. We also use wavelet transforms and embed them as a layer in the architecture to help in denoising the radar images and refine ice layer detection. Our results show that incorporating wavelets in CNNs helps in detecting the position of deep subsurface ice layers, which can be used to analyse their change overtime. ... Article in Journal/Newspaper Ice cap Ice Sheet DataCite Metadata Store (German National Library of Science and Technology) |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
English |
topic |
global temperatures convolutional neural networks ice cap reduction |
spellingShingle |
global temperatures convolutional neural networks ice cap reduction Varshney, Debvrat Yari, Masoud Chowdhury, Tashnim Rahnemoonfar, Maryam Refining Ice Layer Tracking through Wavelet combined Neural Networks (Papers Track) ... |
topic_facet |
global temperatures convolutional neural networks ice cap reduction |
description |
Tackling Climate Change with Machine Learning Workshop at ICML 2021. ... : Rise in global temperatures is resulting in polar ice caps to melt away, which can lead to drastic sea level rise and coastal floods. Accurate calculation of the ice cap reduction is necessary in order to project its climatic impact. Ice sheets are monitored through Snow Radar sensors which give noisy profiles of subsurface ice layers. The sensors take snapshots of the entire ice sheet regularly, and thus result in large datasets. In this work, we use convolutional neural networks (CNNs) for their property of feature extraction and generalizability on large datasets. We also use wavelet transforms and embed them as a layer in the architecture to help in denoising the radar images and refine ice layer detection. Our results show that incorporating wavelets in CNNs helps in detecting the position of deep subsurface ice layers, which can be used to analyse their change overtime. ... |
format |
Article in Journal/Newspaper |
author |
Varshney, Debvrat Yari, Masoud Chowdhury, Tashnim Rahnemoonfar, Maryam |
author_facet |
Varshney, Debvrat Yari, Masoud Chowdhury, Tashnim Rahnemoonfar, Maryam |
author_sort |
Varshney, Debvrat |
title |
Refining Ice Layer Tracking through Wavelet combined Neural Networks (Papers Track) ... |
title_short |
Refining Ice Layer Tracking through Wavelet combined Neural Networks (Papers Track) ... |
title_full |
Refining Ice Layer Tracking through Wavelet combined Neural Networks (Papers Track) ... |
title_fullStr |
Refining Ice Layer Tracking through Wavelet combined Neural Networks (Papers Track) ... |
title_full_unstemmed |
Refining Ice Layer Tracking through Wavelet combined Neural Networks (Papers Track) ... |
title_sort |
refining ice layer tracking through wavelet combined neural networks (papers track) ... |
publisher |
Maryland Shared Open Access Repository |
publishDate |
2021 |
url |
https://dx.doi.org/10.13016/m2p5lq-u1h8 https://mdsoar.org/handle/11603/22312 |
genre |
Ice cap Ice Sheet |
genre_facet |
Ice cap Ice Sheet |
op_rights |
This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. |
op_doi |
https://doi.org/10.13016/m2p5lq-u1h8 |
_version_ |
1775351652657659904 |