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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Veröffentlicht in:	IEEE journal of selected topics in applied earth observations and remote sensing Jg. 13; S. 5326 - 5350
Hauptverfasser:	Amani, Meisam, Ghorbanian, Arsalan, Ahmadi, Seyed Ali, Kakooei, Mohammad, Moghimi, Armin, Mirmazloumi, S. Mohammad, Moghaddam, Sayyed Hamed Alizadeh, Mahdavi, Sahel, Ghahremanloo, Masoud, Parsian, Saeid, Wu, Qiusheng, Brisco, Brian
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Artificial satellites Big Data Classification Cloud computing Data Data analysis Data processing Datasets Earth Engines Environmental monitoring Google Google Earth Engine (GEE) Hydrology Image classification Image processing Land cover Land use Land use classification Land use management Landsat Landsat satellites Learning algorithms Machine learning Natural disasters Remote sensing remote sensing (RS) Satellite observation Urban planning
ISSN:	1939-1404, 2151-1535
Online-Zugang:	Volltext
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Abstract	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 platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was 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 and May 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.
AbstractList	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 platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was 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 and May 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.
Author	Brisco, Brian Amani, Meisam Kakooei, Mohammad Mirmazloumi, S. Mohammad Wu, Qiusheng Ahmadi, Seyed Ali Moghimi, Armin Ghorbanian, Arsalan Mahdavi, Sahel Parsian, Saeid Ghahremanloo, Masoud Moghaddam, Sayyed Hamed Alizadeh
Author_xml	– sequence: 1 givenname: Meisam orcidid: 0000-0002-9495-4010 surname: Amani fullname: Amani, Meisam email: meisam.amani@woodplc.com organization: Wood Environment & Infrastructure Solutions, Ottawa, ON, Canada – sequence: 2 givenname: Arsalan orcidid: 0000-0001-8406-683X surname: Ghorbanian fullname: Ghorbanian, Arsalan email: a.ghorbanian@email.kntu.ac.ir organization: Faculty of Geodesy and Geomatics Engineering, Department of Remote Sensing and Photogrammetry, K. N. Toosi University of Technology, Tehran, Iran – sequence: 3 givenname: Seyed Ali orcidid: 0000-0003-3920-2390 surname: Ahmadi fullname: Ahmadi, Seyed Ali email: cpt.ahmadisnipiol@yahoo.com organization: Faculty of Geodesy and Geomatics Engineering, Department of Remote Sensing and Photogrammetry, K. N. Toosi University of Technology, Tehran, Iran – sequence: 4 givenname: Mohammad orcidid: 0000-0002-2318-8216 surname: Kakooei fullname: Kakooei, Mohammad email: kakooei.mohammad@stu.nit.ac.ir organization: Department of Electronic Engineering, Babol Noshirvani University of Technology, Babol, Iran – sequence: 5 givenname: Armin orcidid: 0000-0002-0455-4882 surname: Moghimi fullname: Moghimi, Armin email: moghimi.armin@gmail.com organization: Faculty of Geodesy and Geomatics Engineering, Department of Remote Sensing and Photogrammetry, K. N. Toosi University of Technology, Tehran, Iran – sequence: 6 givenname: S. Mohammad orcidid: 0000-0001-5310-5859 surname: Mirmazloumi fullname: Mirmazloumi, S. Mohammad email: sm.mirmazloumi@cttc.es organization: Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Castelldefels, Spain – sequence: 7 givenname: Sayyed Hamed Alizadeh orcidid: 0000-0003-2992-4277 surname: Moghaddam fullname: Moghaddam, Sayyed Hamed Alizadeh email: h.alizadeh@email.kntu.ac.ir organization: Faculty of Geodesy and Geomatics Engineering, Department of Remote Sensing and Photogrammetry, K. N. Toosi University of Technology, Tehran, Iran – sequence: 8 givenname: Sahel orcidid: 0000-0002-1670-151X surname: Mahdavi fullname: Mahdavi, Sahel email: sahel.mahdavi@woodplc.com organization: Wood Environment & Infrastructure Solutions, Ottawa, ON, Canada – sequence: 9 givenname: Masoud orcidid: 0000-0001-9971-3892 surname: Ghahremanloo fullname: Ghahremanloo, Masoud email: mghahremanloo@uh.edu organization: Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX, USA – sequence: 10 givenname: Saeid surname: Parsian fullname: Parsian, Saeid email: saeid90parsian@gmail.com organization: Department of Surveying Engineering, Tafresh University, Tafresh, Iran – sequence: 11 givenname: Qiusheng orcidid: 0000-0001-5437-4073 surname: Wu fullname: Wu, Qiusheng email: qwu18@utk.edu organization: Department of Geography, University of Tennessee, Knoxville, TN, USA – sequence: 12 givenname: Brian orcidid: 0000-0001-8439-362X surname: Brisco fullname: Brisco, Brian email: brian.brisco@canada.ca organization: Canada Center for Mapping and Earth Observation, Ottawa, ON, Canada
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ContentType	Journal Article
Copyright	Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020
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SubjectTerms	Algorithms Artificial satellites Big Data Classification Cloud computing Data Data analysis Data processing Datasets Earth Engines Environmental monitoring Google Google Earth Engine (GEE) Hydrology Image classification Image processing Land cover Land use Land use classification Land use management Landsat Landsat satellites Learning algorithms Machine learning Natural disasters Remote sensing remote sensing (RS) Satellite observation Urban planning
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Title	Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review
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