Google Earth Engine and Cloud Computing Platforms: Methods and Applications in Big Geo Data Science

Dear Colleagues,

According to the well-known sentence “80% of data is geographic”, much of the data in the world can be geo-referenced. Geospatial data describe objects and things with locations given in a spatial reference frame, which now generally means the global spatial reference frame (often called WGS84), which is connected to the Global Navigation Satellite Systems. Geospatial data can be collected and analyzed using a variety of geomatic sensors and methodologies (GNSS and terrestrial surveying, photogrammetry and remote sensing, laser scanning, mobile mapping, geo-located sensors, geo-tagged web contents, and volunteered geographic information—VGI). Among them, those related to remote sensing play a pivotal role, since petabyte-scale archives of remote sensing data have become freely available from the EU Copernicus Program and multiple U.S. Government agencies (NASA, USGS, and NOAA).

This is the why the efficient geospatial big data handling, particularly remote sensing data, is of key importance. In this respect, it is necessary to change the way these data are visualized, processed, and analyzed, in order to make them truly available to the wide community of non-remote sensing experts, who indeed need remote sensing big data to investigate, monitor, and model a large and continuously growing variety of Earth systems, social, and economic processes.

Currently, cloud infrastructures can provide the required flexibility to manage (for both storage and computation) such huge amounts of data and to efficiently process them, thus making possible analyses that were previously thought unfeasible, due to data volume and computational restrictions. In this respect, Google Earth Engine (GEE) is a cloud-based platform that makes it easy to access both multi-temporal remote sensing big data and high-performance computing resources for processing these datasets. Also, GEE users can upload their own non-public data in reserved areas and process them together the public ones, performing synergic data fusion and integration. GEE is also designed to help researchers easily disseminate their results to other researchers, policy makers, and even the general public, to support a variety of management decisions or simply to share scientific results.

Research papers focusing on both methodology and applications by using GEE across different geographic scales are welcome, as well as contributions related to other public-domain platforms with goals similar to GEE (i.e., ESA DIAS and ESA TEPs—Thematic Exploitation Platforms).

Potential topics for this Special Issue include but are not limited to the following:

• Remote Sensing Big Data analysis and integration with other geospatial data (i.e., GNSS, social media data);
• Multi-Sensor and multi-resolution data analysis;
• Machine and deep learning for remote sensing;
• Land-use and land-cover change monitoring and modeling;
• Urban and population dynamics characterization;
• Water resources monitoring and modeling;
• Forests and vegetation dynamics monitoring and modeling;
• Ecosystem response to the climate change.

Prof. Mattia Crespi
Dr. Andrea Nascetti
Dr. Roberta Ravanelli
Guest Editors

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