Vol. 12, Issue 3 (2023)

Author(s):
Ankur P Vora, Dr. Chetan R Dudhagara, Dr. DR Kathiriya and Dr. RS Pundir

Abstract:
Massive amounts of data have been gathered by remote sensing (RS) systems for decades, but managing and analyzing them with standard software and desktop computing resources is impractical. To effectively handle the difficulties of big data analytics, Google has created a cloud computing platform called Google Earth Engine (GEE). This platform makes it possible to process big geodata over vast areas and to continuously monitor the environment. The GEE offers access to petabytes of publicly accessible remote sensing datasets and machine learning algorithms using Google's computational infrastructure and a library of Application Programming Interfaces (APIs) with development environments that support well-known coding languages, such as JavaScript and Python. Together, these fundamental components give users the ability to analyze and visualize big data without access to supercomputers or specialized coding knowledge. However, ten years after its inception, the effects of GEE on data analytics have not been properly investigated. As a result, a systematic review of GEE is required to provide readers with the "big picture" of the current state and general trends in GEE. GEE has also been used in a wide variety of applications, including land use/land cover classification, hydrology, urban planning, natural disasters, climate analyses and image processing.