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 22131
Special Issue Editor
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
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Keywords
- google earth engine
- geo-big data
- environmental monitoring
- machine learning
- cloud computing
- satellite imagery
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