Google Earth Engine: Cloud-Based Platform for Earth Observation Data and Analysis
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: closed (15 January 2022) | Viewed by 90182
Special Issue Editors
Interests: SAR; LiDAR; wetlands; machine learning; random forest
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; cryosphere; hydrology; environment; mountains; cold regions; change detection; data science
Special Issue Information
Dear Colleagues,
The ever-increasing global archive of earth observation data enables environmental and societal problems to be assessed and issues to be monitored globally and over long time-scales. Remote sensing software and applications must be able to perform large-area and time-series analysis in a timely manner and at meaningful scales. However, at these spatial and temporal extents, traditional remote sensing workflows that include downloading, processing, and analyzing data locally become challenging for individual scientists and institutions, requiring terabytes to petabytes of storage space and expertise in server or cloud-based storage and processing systems.
Google Earth Engine (GEE) enables free programmatic access to the MODIS, Landsat 1-5,7, and 8, and Sentinel-1, 2, 3, and 5 archives, with continual updates, as well as many other imagery and ancillary datasets (e.g., land-use data, climate and soil data), through either a Javascript or Python API. As only a browser and internet access is required, these platforms enable access to earth observation data by a new generation of analysts, without the requirement of expensive infrastructure and software. Google provides free training and example codes online to easily enable access to the basic data and algorithms exposed through GEE, and the GEE user community has posted thousands of code and workflow examples online, allowing users to adopt a wide variety of different processing and analysis techniques. Recently, the addition of access to TensorFlow through Google CoLabs has exposed advanced data science and machine learning techniques to users of the GEE earth observation archive. Not only does this enable new tools for the remote sensing scientific community, but it also introduces data scientists to earth observation data analysis using familiar tools and platforms.
For this Special Issue, we are soliciting contributions that demonstrate new algorithms, methods or applications implemented in either of the GEE APIs. We particularly encourage studies that introduce new analysis techniques, address challenges in implementing large-scale and/or long-time series analysis, and those that share code or application examples. While the main focus of this Special Issue will be on methodological advances using GEE, site-specific case studies that employ GEE functions or tools to advance scientific understanding of environmental and societal issues are also welcome.
Dr. Koreen MillardMr. Alexandre R. Bevington
Guest Editors
Manuscript Submission Information
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Keywords
- Google Earth Engine
- Environmental change
- Cloud computing
- Big data
- Data democratization
- Machine learning
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