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CIRA annual report FY 2016/2017
Reporting period April 1, 2016-March 31, 2017
CIRA annual report FY 2017/2018
Reporting period April 1, 2017-March 31, 2018
CIRA annual report FY 2014/2015
Reporting period July 1, 2014-March 31, 2015
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), 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 ReviewedPostprint (published version
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