How to leverage AI/ML for Earth Science Research: An Open Science Approach with Arctic Sea Ice

•Sea ice in polar region have become increasingly important due to it’s a sensitive climate change indicator. •High spatial resolution (HSR) imagery is critical in Arctic sea ice research for verifying the satellite data, extracting sea ice physical parameters •This project aims to develop an open s...

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Bibliographic Details
Main Authors: Chaowei Yang, Dexuan Sha, Anusha Srirenganathan, Xin Miao, Hongjie Xie, Younghyun Koo
Format: Other/Unknown Material
Language:unknown
Published: 2022
Subjects:
AI
Online Access:https://zenodo.org/record/6874974
https://doi.org/10.5281/zenodo.6874974
id ftzenodo:oai:zenodo.org:6874974
record_format openpolar
spelling ftzenodo:oai:zenodo.org:6874974 2023-05-15T14:35:53+02:00 How to leverage AI/ML for Earth Science Research: An Open Science Approach with Arctic Sea Ice Chaowei Yang Dexuan Sha Anusha Srirenganathan Xin Miao Hongjie Xie Younghyun Koo 2022-07-21 https://zenodo.org/record/6874974 https://doi.org/10.5281/zenodo.6874974 unknown doi:10.5281/zenodo.6874973 https://zenodo.org/communities/cssi2022 https://zenodo.org/record/6874974 https://doi.org/10.5281/zenodo.6874974 oai:zenodo.org:6874974 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode sea ice arctic climate change AI training datasets online service info:eu-repo/semantics/other publication-other 2022 ftzenodo https://doi.org/10.5281/zenodo.687497410.5281/zenodo.6874973 2023-03-11T02:03:38Z •Sea ice in polar region have become increasingly important due to it’s a sensitive climate change indicator. •High spatial resolution (HSR) imagery is critical in Arctic sea ice research for verifying the satellite data, extracting sea ice physical parameters •This project aims to develop an open science approach to collect and process the HSR data, integrate knowledge, and analyze scientific phenomena to support polar sciences. Other/Unknown Material Arctic Climate change Sea ice Zenodo Arctic
institution Open Polar
collection Zenodo
op_collection_id ftzenodo
language unknown
topic sea ice
arctic
climate change
AI
training datasets
online service
spellingShingle sea ice
arctic
climate change
AI
training datasets
online service
Chaowei Yang
Dexuan Sha
Anusha Srirenganathan
Xin Miao
Hongjie Xie
Younghyun Koo
How to leverage AI/ML for Earth Science Research: An Open Science Approach with Arctic Sea Ice
topic_facet sea ice
arctic
climate change
AI
training datasets
online service
description •Sea ice in polar region have become increasingly important due to it’s a sensitive climate change indicator. •High spatial resolution (HSR) imagery is critical in Arctic sea ice research for verifying the satellite data, extracting sea ice physical parameters •This project aims to develop an open science approach to collect and process the HSR data, integrate knowledge, and analyze scientific phenomena to support polar sciences.
format Other/Unknown Material
author Chaowei Yang
Dexuan Sha
Anusha Srirenganathan
Xin Miao
Hongjie Xie
Younghyun Koo
author_facet Chaowei Yang
Dexuan Sha
Anusha Srirenganathan
Xin Miao
Hongjie Xie
Younghyun Koo
author_sort Chaowei Yang
title How to leverage AI/ML for Earth Science Research: An Open Science Approach with Arctic Sea Ice
title_short How to leverage AI/ML for Earth Science Research: An Open Science Approach with Arctic Sea Ice
title_full How to leverage AI/ML for Earth Science Research: An Open Science Approach with Arctic Sea Ice
title_fullStr How to leverage AI/ML for Earth Science Research: An Open Science Approach with Arctic Sea Ice
title_full_unstemmed How to leverage AI/ML for Earth Science Research: An Open Science Approach with Arctic Sea Ice
title_sort how to leverage ai/ml for earth science research: an open science approach with arctic sea ice
publishDate 2022
url https://zenodo.org/record/6874974
https://doi.org/10.5281/zenodo.6874974
geographic Arctic
geographic_facet Arctic
genre Arctic
Climate change
Sea ice
genre_facet Arctic
Climate change
Sea ice
op_relation doi:10.5281/zenodo.6874973
https://zenodo.org/communities/cssi2022
https://zenodo.org/record/6874974
https://doi.org/10.5281/zenodo.6874974
oai:zenodo.org:6874974
op_rights info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/4.0/legalcode
op_doi https://doi.org/10.5281/zenodo.687497410.5281/zenodo.6874973
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