AICE: AI Solutions for the Arctic & Antarctic Discovery
The objective of this research is to investigate artificial intelligence (AI) solutions for data collected by the Center for Remote Sensing of Ice Sheets (CReSIS) in order to provide an intelligent data understanding to automatically mine and analyze the heterogeneous dataset collected by CReSIS and...
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ftdatacite:10.6084/m9.figshare.13341836.v1 2023-05-15T13:52:31+02:00 AICE: AI Solutions for the Arctic & Antarctic Discovery Rahnemoonfar, Maryam 2020 https://dx.doi.org/10.6084/m9.figshare.13341836.v1 https://esip.figshare.com/articles/presentation/AICE_AI_Solutions_for_the_Arctic_Antarctic_Discovery/13341836/1 unknown ESIP https://dx.doi.org/10.6084/m9.figshare.13341836 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY Artificial Intelligence and Image Processing FOS Computer and information sciences 80104 Computer Vision Presentation MediaObject article Audiovisual 2020 ftdatacite https://doi.org/10.6084/m9.figshare.13341836.v1 https://doi.org/10.6084/m9.figshare.13341836 2021-11-05T12:55:41Z The objective of this research is to investigate artificial intelligence (AI) solutions for data collected by the Center for Remote Sensing of Ice Sheets (CReSIS) in order to provide an intelligent data understanding to automatically mine and analyze the heterogeneous dataset collected by CReSIS and Operation IceBridge mission at NASA. Significant resources have been and will be spent in collecting and storing large and heterogeneous datasets from expensive Arctic and Antarctic fieldwork (e.g. through NSF Big Idea: Navigating the New Arctic). While traditional analyses provide some insight, the complexity, scale, and multidisciplinary nature of the data necessitate advanced intelligent solutions. This project will allow domain scientists to automatically answer questions about the properties of the data, including ice thickness, ice surface, ice bottom, internal layers, ice thickness prediction, and bedrock visualization. The planned approach will advance the broader big data research community by improving the efficiency of deep learning methods and in the investigation of methods to merge data-driven AI approaches with application-specific domain knowledge. Conference Object Antarc* Antarctic Arctic Center for Remote Sensing of Ice Sheets (CReSIS) DataCite Metadata Store (German National Library of Science and Technology) Arctic Antarctic |
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DataCite Metadata Store (German National Library of Science and Technology) |
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language |
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topic |
Artificial Intelligence and Image Processing FOS Computer and information sciences 80104 Computer Vision |
spellingShingle |
Artificial Intelligence and Image Processing FOS Computer and information sciences 80104 Computer Vision Rahnemoonfar, Maryam AICE: AI Solutions for the Arctic & Antarctic Discovery |
topic_facet |
Artificial Intelligence and Image Processing FOS Computer and information sciences 80104 Computer Vision |
description |
The objective of this research is to investigate artificial intelligence (AI) solutions for data collected by the Center for Remote Sensing of Ice Sheets (CReSIS) in order to provide an intelligent data understanding to automatically mine and analyze the heterogeneous dataset collected by CReSIS and Operation IceBridge mission at NASA. Significant resources have been and will be spent in collecting and storing large and heterogeneous datasets from expensive Arctic and Antarctic fieldwork (e.g. through NSF Big Idea: Navigating the New Arctic). While traditional analyses provide some insight, the complexity, scale, and multidisciplinary nature of the data necessitate advanced intelligent solutions. This project will allow domain scientists to automatically answer questions about the properties of the data, including ice thickness, ice surface, ice bottom, internal layers, ice thickness prediction, and bedrock visualization. The planned approach will advance the broader big data research community by improving the efficiency of deep learning methods and in the investigation of methods to merge data-driven AI approaches with application-specific domain knowledge. |
format |
Conference Object |
author |
Rahnemoonfar, Maryam |
author_facet |
Rahnemoonfar, Maryam |
author_sort |
Rahnemoonfar, Maryam |
title |
AICE: AI Solutions for the Arctic & Antarctic Discovery |
title_short |
AICE: AI Solutions for the Arctic & Antarctic Discovery |
title_full |
AICE: AI Solutions for the Arctic & Antarctic Discovery |
title_fullStr |
AICE: AI Solutions for the Arctic & Antarctic Discovery |
title_full_unstemmed |
AICE: AI Solutions for the Arctic & Antarctic Discovery |
title_sort |
aice: ai solutions for the arctic & antarctic discovery |
publisher |
ESIP |
publishDate |
2020 |
url |
https://dx.doi.org/10.6084/m9.figshare.13341836.v1 https://esip.figshare.com/articles/presentation/AICE_AI_Solutions_for_the_Arctic_Antarctic_Discovery/13341836/1 |
geographic |
Arctic Antarctic |
geographic_facet |
Arctic Antarctic |
genre |
Antarc* Antarctic Arctic Center for Remote Sensing of Ice Sheets (CReSIS) |
genre_facet |
Antarc* Antarctic Arctic Center for Remote Sensing of Ice Sheets (CReSIS) |
op_relation |
https://dx.doi.org/10.6084/m9.figshare.13341836 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.6084/m9.figshare.13341836.v1 https://doi.org/10.6084/m9.figshare.13341836 |
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1766256831136530432 |