Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review

Remote sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE...

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Detalles Bibliográficos
Autor: Mirmazloumi, Seyed Mohammad
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/359277
Acceso en línea:https://hdl.handle.net/2117/359277
https://dx.doi.org/10.1109/JSTARS.2020.3021052
Access Level:acceso abierto
Palabra clave:Cloud computing
Web services
Geographical positions
Google Earth
Big data
Google Earth Engine (GEE)
Remote sensing (RS)
Computació en núvol
Google+ (Recurs electrònic)
Àrees temàtiques de la UPC::Informàtica::Programació
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spelling Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive reviewMirmazloumi, Seyed MohammadCloud computingWeb servicesGeographical positionsGoogle EarthBig dataCloud computingGoogle Earth Engine (GEE)Remote sensing (RS)Computació en núvolGoogle+ (Recurs electrònic)Àrees temàtiques de la UPC::Informàtica::ProgramacióRemote sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platformfacilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platformwas launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 andMay 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest, were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications have significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.Peer ReviewedInstitute of Electrical and Electronics Engineers (IEEE)20202020-01-0120222022-01-11journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/359277https://dx.doi.org/10.1109/JSTARS.2020.3021052reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3592772026-05-27T15:37:01Z
dc.title.none.fl_str_mv Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review
title Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review
spellingShingle Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review
Mirmazloumi, Seyed Mohammad
Cloud computing
Web services
Geographical positions
Google Earth
Big data
Cloud computing
Google Earth Engine (GEE)
Remote sensing (RS)
Computació en núvol
Google+ (Recurs electrònic)
Àrees temàtiques de la UPC::Informàtica::Programació
title_short Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review
title_full Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review
title_fullStr Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review
title_full_unstemmed Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review
title_sort Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review
dc.creator.none.fl_str_mv Mirmazloumi, Seyed Mohammad
author Mirmazloumi, Seyed Mohammad
author_facet Mirmazloumi, Seyed Mohammad
author_role author
dc.subject.none.fl_str_mv Cloud computing
Web services
Geographical positions
Google Earth
Big data
Cloud computing
Google Earth Engine (GEE)
Remote sensing (RS)
Computació en núvol
Google+ (Recurs electrònic)
Àrees temàtiques de la UPC::Informàtica::Programació
topic Cloud computing
Web services
Geographical positions
Google Earth
Big data
Cloud computing
Google Earth Engine (GEE)
Remote sensing (RS)
Computació en núvol
Google+ (Recurs electrònic)
Àrees temàtiques de la UPC::Informàtica::Programació
description Remote sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platformfacilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platformwas launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 andMay 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest, were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications have significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-01-01
2022
2022-01-11
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/359277
https://dx.doi.org/10.1109/JSTARS.2020.3021052
url https://hdl.handle.net/2117/359277
https://dx.doi.org/10.1109/JSTARS.2020.3021052
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301629

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