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Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Meisam AmaniArsalan GhorbanianFaculty of Geodesy and Geomatics Engineering, Department of Remote Sensing and Photogrammetry, K. N. Toosi University of Technology, Tehran, IranSeyed Ali AhmadiFaculty of Geodesy and Geomatics Engineering, Department of Remote Sensing and Photogrammetry, K. N. Toosi University of Technology, Tehran, IranMohammad KakooeiDepartment of Electronic Engineering, Babol Noshirvani University of Technology, Babol, IranArmin MoghimiFaculty of Geodesy and Geomatics Engineering, Department of Remote Sensing and Photogrammetry, K. N. Toosi University of Technology, Tehran, IranS. Mohammad MirmazloumiCentre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Castelldefels, SpainSayyed Hamed Alizadeh MoghaddamFaculty of Geodesy and Geomatics Engineering, Department of Remote Sensing and Photogrammetry, K. N. Toosi University of Technology, Tehran, IranSahel MahdaviMasoud GhahremanlooDepartment of Earth and Atmospheric Sciences, University of Houston, Houston, TX, USASaeid ParsianDepartment of Surveying Engineering, Tafresh University, Tafresh, IranQiusheng WuDepartment of Geography, University of Tennessee, Knoxville, TN, USABrian Brisco

2020en

ABI

Annotatsiya

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.

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DOI: 10.1109/jstars.2020.3021052

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