Google Earth Engine: A Global Analysis and Future Trends

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 14; p. 3675
• Main Authors: Velastegui-Montoya, Andrés, Montalván-Burbano, Néstor, Carrión-Mero, Paúl, Rivera-Torres, Hugo, Sadeck, Luís, Adami, Marcos
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2023
• Subjects: Academic disciplines; Algorithms; Analysis; bibliometric analysis; Bibliometrics; Big Data; Citation indexes; Cloud computing; co-citation analysis; Cocitation; Cognitive ability; Computer science; Computers; Data analysis; Data collection; Datasets; Earth; earth engine; Forest & brush fires; Geographic information systems; geoprocessing; Geospatial data; Keywords; knowledge mapping; Land cover; Land use; Literature reviews; Machine learning; Remote sensing; Remote sensors; Research methodology; Satellites; Scientometrics; Software; Spatial data; Trends; Brazil; Canada; United Kingdom; Netherlands; China; India
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15143675

The continuous increase in the volume of geospatial data has led to the creation of storage tools and the cloud to process data. Google Earth Engine (GEE) is a cloud-based platform that facilitates geoprocessing, making it a tool of great interest to the academic and research world. This article proposes a bibliometric analysis of the GEE platform to analyze its scientific production. The methodology consists of four phases. The first phase corresponds to selecting “search” criteria, followed by the second phase focused on collecting data during the 2011 and 2022 periods using Elsevier’s Scopus database. Software and bibliometrics allowed to review the published articles during the third phase. Finally, the results were analyzed and interpreted in the last phase. The research found 2800 documents that received contributions from 125 countries, with China and the USA leading as the countries with higher contributions supporting an increment in the use of GEE for the visualization and processing of geospatial data. The intellectual structure study and knowledge mapping showed that topics of interest included satellites, sensors, remote sensing, machine learning, land use and land cover. The co-citations analysis revealed the connection between the researchers who used the GEE platform in their research papers. GEE has proven to be an emergent web platform with the potential to manage big satellite data easily. Furthermore, GEE is considered a multidisciplinary tool with multiple applications in various areas of knowledge. This research adds to the current knowledge about the Google Earth Engine platform, analyzing its cognitive structure related to the research in the Scopus database. In addition, this study presents inferences and suggestions to develop future works with this methodology.