Details
| Original language | English |
|---|---|
| Article number | 9184118 |
| Pages (from-to) | 5326-5350 |
| Number of pages | 25 |
| Journal | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Volume | 13 |
| Publication status | Published - 2020 |
| Externally published | Yes |
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.
Keywords
- Big data, cloud computing, Google Earth Engine (GEE), remote sensing (RS)
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Computers in Earth Sciences
- Earth and Planetary Sciences(all)
- Atmospheric Science
Sustainable Development Goals
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In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 13, 9184118, 2020, p. 5326-5350.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications
T2 - A Comprehensive Review
AU - Amani, Meisam
AU - Ghorbanian, Arsalan
AU - Ahmadi, Seyed Ali
AU - Kakooei, Mohammad
AU - Moghimi, Armin
AU - Mirmazloumi, S. Mohammad
AU - Moghaddam, Sayyed Hamed Alizadeh
AU - Mahdavi, Sahel
AU - Ghahremanloo, Masoud
AU - Parsian, Saeid
AU - Wu, Qiusheng
AU - Brisco, Brian
N1 - Publisher Copyright: © 2008-2012 IEEE.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Big data
KW - cloud computing
KW - Google Earth Engine (GEE)
KW - remote sensing (RS)
UR - http://www.scopus.com/inward/record.url?scp=85092080246&partnerID=8YFLogxK
U2 - 10.1109/JSTARS.2020.3021052
DO - 10.1109/JSTARS.2020.3021052
M3 - Article
AN - SCOPUS:85092080246
VL - 13
SP - 5326
EP - 5350
JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
SN - 1939-1404
M1 - 9184118
ER -