Modelling inland Arctic bathymetry from space using cloud-based machine learning and Sentinel-2

Bibliographic Details
Published in:Advances in Space Research
Main Author: Merchant, Michael A.
Other Authors: Ducks Unlimited Canada, US Fish and Wildlife Service, Naval Air Warfare Center, Aircraft Division
Format: Article in Journal/Newspaper
Language:English
Published: Elsevier BV 2023
Subjects:
Space and Planetary Science
Aerospace Engineering
General Earth and Planetary Sciences
Atmospheric Science
Geophysics
Astronomy and Astrophysics
Arctic
Online Access:http://dx.doi.org/10.1016/j.asr.2023.07.064
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spelling crelsevierbv:10.1016/j.asr.2023.07.064 2024-05-19T07:35:04+00:00 Modelling inland Arctic bathymetry from space using cloud-based machine learning and Sentinel-2 Merchant, Michael A. Ducks Unlimited Canada US Fish and Wildlife Service Naval Air Warfare Center, Aircraft Division 2023 http://dx.doi.org/10.1016/j.asr.2023.07.064 https://api.elsevier.com/content/article/PII:S0273117723006130?httpAccept=text/xml https://api.elsevier.com/content/article/PII:S0273117723006130?httpAccept=text/plain en eng Elsevier BV https://www.elsevier.com/tdm/userlicense/1.0/ Advances in Space Research volume 72, issue 10, page 4256-4271 ISSN 0273-1177 Space and Planetary Science Aerospace Engineering General Earth and Planetary Sciences Atmospheric Science Geophysics Astronomy and Astrophysics journal-article 2023 crelsevierbv https://doi.org/10.1016/j.asr.2023.07.064 2024-04-19T06:51:05Z Article in Journal/Newspaper Arctic ScienceDirect (Elsevier) Advances in Space Research
institution Open Polar
collection ScienceDirect (Elsevier)
op_collection_id crelsevierbv
language English
topic Space and Planetary Science
Aerospace Engineering
General Earth and Planetary Sciences
Atmospheric Science
Geophysics
Astronomy and Astrophysics
spellingShingle Space and Planetary Science
Aerospace Engineering
General Earth and Planetary Sciences
Atmospheric Science
Geophysics
Astronomy and Astrophysics
Merchant, Michael A.
Modelling inland Arctic bathymetry from space using cloud-based machine learning and Sentinel-2
topic_facet Space and Planetary Science
Aerospace Engineering
General Earth and Planetary Sciences
Atmospheric Science
Geophysics
Astronomy and Astrophysics
author2 Ducks Unlimited Canada
US Fish and Wildlife Service
Naval Air Warfare Center, Aircraft Division
format Article in Journal/Newspaper
author Merchant, Michael A.
author_facet Merchant, Michael A.
author_sort Merchant, Michael A.
title Modelling inland Arctic bathymetry from space using cloud-based machine learning and Sentinel-2
title_short Modelling inland Arctic bathymetry from space using cloud-based machine learning and Sentinel-2
title_full Modelling inland Arctic bathymetry from space using cloud-based machine learning and Sentinel-2
title_fullStr Modelling inland Arctic bathymetry from space using cloud-based machine learning and Sentinel-2
title_full_unstemmed Modelling inland Arctic bathymetry from space using cloud-based machine learning and Sentinel-2
title_sort modelling inland arctic bathymetry from space using cloud-based machine learning and sentinel-2
publisher Elsevier BV
publishDate 2023
url http://dx.doi.org/10.1016/j.asr.2023.07.064
https://api.elsevier.com/content/article/PII:S0273117723006130?httpAccept=text/xml
https://api.elsevier.com/content/article/PII:S0273117723006130?httpAccept=text/plain
genre Arctic
genre_facet Arctic
op_source Advances in Space Research
volume 72, issue 10, page 4256-4271
ISSN 0273-1177
op_rights https://www.elsevier.com/tdm/userlicense/1.0/
op_doi https://doi.org/10.1016/j.asr.2023.07.064
container_title Advances in Space Research
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