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Special Issue "Google Earth Engine for Geo-Big Data Applications"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation Data".

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 5479

Special Issue Editor

Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada
Interests: remote sensing; geospatial data; machine learning; geo big data; wetland; GHG monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A multi-petabyte collection of geospatial datasets and satellite imagery are combined with planetary-scale analysis tools in a web platform called the Google Earth Engine (GEE). Today, the Earth Engine is extensively used for geospatial data processing by many scientists, researchers, and developers for several tasks, including Earth Observation (EO) data preparation, image classification, change detection, environmental applications, and even geospatial data visualization. This Special Issue provides an opportunity to bring together research in “Geo-Big Data Analysis using GEE” and highlights ongoing investigations and new applications of geo-big data. In particular, this issue is designed to highlight currently applied research using satellite and Geospatial data processing using GEE to better understand and solve environmental problems at regional, national, and global scales. As such, authors are encouraged to submit high-quality, original research that demonstrates new algorithms, methods, or applications implemented in GEE using geo-big data. We are interested in studies that introduce new techniques for geo-big data analysis, address the challenges of using time series of EO data, and share codes and examples. Furthermore, review papers on environmental monitoring using GEE and geo-big data, as well as case-specific studies that use GEE functions and tools for increasing the scientific understanding of environmental challenges are also welcome.

Potential topics for original research papers and review articles using GEE and geo-big data include, but are not limited to, the following:

  • Land Use and Land Cover (LULC) classification and change detection from a regional to a global scale;
  • Machine learning and deep learning for geo-big data analysis;
  • Crop mapping and yield estimation;
  • Wetland and water resource management from a regional to a global scale;
  • Forest monitoring and biomass estimation;
  • Multi-source and multi-resolution geo-big data analysis;
  • Climate change;
  • Green House Gas (GHG) emission monitoring from a regional to a global scale.

Dr. Masoud Mahdianpari
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • google earth engine
  • geo-big data
  • environmental monitoring
  • machine learning
  • cloud computing
  • satellite imagery

Published Papers (1 paper)

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Google Earth Engine: A Global Analysis and Future Trends
Remote Sens. 2023, 15(14), 3675; - 23 Jul 2023
Viewed by 5191
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Google Earth Engine for Geo-Big Data Applications)
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