Special Issue on “Cloud Computing in Google Earth Engine for Remote Sensing”
- January 1, 2020
- IEEE JSTARS
This special issue will collect the most recent research works related to different aspects of cloud computing within GEE platform for remote sensing.
General Information
Earth observation satellites have been collecting remote sensing data for decades, enabling us to map, monitor, and study Earth’s systems and environments. The massive volumes of satellite data have brought us both opportunities and challenges. Nowadays, it is impractical to download, manage, and analyze a large volume of time-series satellite images using desktop computing resources. The advent of cloud computing platforms, such as Google Earth Engine (GEE), makes it possible to access and analyze large volumes of analysis-ready geospatial datasets without having to download them to local computers. The GEE data catalog contains a large repository of publicly available datasets acquired from various satellite platforms and aerial imaging systems (e.g., Landsat, Sentinel, MODIS, NAIP), which are being updated and expanded on a daily basis. In addition, GEE cloud platform provides a set of efficient algorithms for analyzing and visualizing satellite images, such as compositing, cloud masking, mosaicking, supervised and unsupervised classifications. Last but not least, users can upload their own datasets to this cloud computing platform and develop algorithms using JavaScript or Python. This special issue aims to collect the most recent research works related to different aspects of cloud computing within GEE platform for remote sensing.
Topics
- Recent developments in GEE cloud computing for remote sensing
- Cloud storage and big remote sensing data management in GEE
- New machine learning algorithms for cloud computing in GEE
- Deep Learning in GEE cloud computing platform
- Cloud computing for land cover/land use mapping
- Change detection using big data in cloud
- Cloud-based hydrology and water resource management
- Cloud-based climate and weather analyses
- GEE for oceanographic applications
- Global-scale analyses using big data processing and cloud computing in GEE
Schedule
January 1, 2020: Submission system opening
October 31, 2020: Submission system closing
2020: Publication date
Guest Editors
Meisam Amani: Wood Environment & Infrastructure Solutions, Canada (meisam.amani@woodplc.com)
Qiusheng Wu: University of Tennessee, USA (qwu18@utk.edu)
Xiangming Xiao: University of Oklahoma, USA (xiangming.xiao@ou.edu)
Le Yu: Tsinghua University, China (leyu@tsinghua.edu.cn)
Murali Krishna Gumma: International Crops Research Institute for the Semi-Arid Tropics, India (m.gumma@cgiar.org)
