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.
Highlights
I N RECENT years, there has been a significant increase in the number of remote sensing (RS) datasets acquired by various spaceborne and airborne sensors with different characteristics [1]
In this study, 450 journal articles along with peer-reviewed conference papers were investigated through eight main sections: Section I provides an introduction to Google Earth Engine (GEE); Section II provides an overview of the GEE platform; Section III presents different datasets included in this platform; Section IV discusses various GEE functions and algorithms; Section V provides comprehensive information about the advantages and limitations of GEE; Section VI analyzes the pattern of GEE publications over one decade; Section VII discusses different applications of GEE; and Section VIII provides several case studies, in which GEE was applied to process and analyze big data over large areas and within a long period of time
The proliferation of big geo data and the recent advance in cloud computing and big data processing services are changing the future of RS
Summary
I N RECENT years, there has been a significant increase in the number of remote sensing (RS) datasets acquired by various spaceborne and airborne sensors with different characteristics (e.g., spectral, spatial, temporal, and radiometric resolutions) [1]. In this study, 450 journal articles along with peer-reviewed conference papers were investigated through eight main sections: Section I provides an introduction to GEE; Section II provides an overview of the GEE platform; Section III presents different datasets included in this platform; Section IV discusses various GEE functions and algorithms; Section V provides comprehensive information about the advantages and limitations of GEE; Section VI analyzes the pattern of GEE publications over one decade; Section VII discusses different applications of GEE; and Section VIII provides several case studies, in which GEE was applied to process and analyze big data over large areas and within a long period of time
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