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.

中文翻译：

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用于遥感大数据应用的 Google Earth Engine 云计算平台：全面综述


几十年来，遥感（RS）系统一直在收集大量数据集，使用常见的软件包和桌面计算资源来管理和分析这些数据并不现实。对此，谷歌开发了一个云计算平台，称为谷歌地球引擎（GEE），以有效解决大数据分析的挑战。特别是，该平台有利于处理大范围的大地理数据并长期监测环境。尽管该平台于 2010 年推出，并已证明其在不同应用中的巨大潜力，但直到最近几年才得到充分研究和用于遥感应用。因此，本研究旨在全面探讨 GEE 平台的不同方面，包括其数据集、功能、优点/局限性以及各种应用。为此，我们对 2010 年 1 月至 2020 年 5 月期间在 150 种期刊上发表的 450 篇期刊文章进行了研究。据观察，GEE 用户广泛使用了 Landsat 和 Sentinel 数据集。此外，随机森林等监督机器学习算法更广泛地应用于图像分类任务。 GEE 还被用于广泛的应用，例如土地覆盖/土地利用分类、水文学、城市规划、自然灾害、气候分析和图像处理。人们普遍认为，过去几年GEE出版物的数量显着增加，预计GEE将被更多来自不同领域的用户用来解决他们的大数据处理挑战。