Authors :
Arvind Chandrasekaran
Volume/Issue :
Volume 8 - 2023, Issue 3 - March
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/3zGnFNb
DOI :
https://doi.org/10.5281/zenodo.7809303
Abstract :
Multi-faceted remote sensing (SAR) and multiarea datasets are widely adopted because of the up-to-date
resource information and global and regional monitoring
environment. Remote Sensing (RS) data processing
involves a multi-step processing sequence, which includes
independent processing steps depending on the RS
application type. RS data processing for environmental
monitoring and regional disaster is computationally
recognized and has data demand. The combination of
High-Performance Computing and cloud computing
propose an efficient method to solve the problems through
large-scale RS data search processing for several
applications. The elastic, ubiquitous, and high
transparency level of cloud computing enables them to run
massive RS data management and monitor the dynamic
environments on the cloud through the web interface. The
cloud service core provides the parallel file system for
large-scale RS data and as an interface to access RS data
to improve data localization.
Keywords :
Edge Computing; Fog Computing; IoT devices; Multi-faceted and multi-area remote sensing (SAR) datasets; High-Performance Computing; Hilbert-based data indexing.
Multi-faceted remote sensing (SAR) and multiarea datasets are widely adopted because of the up-to-date
resource information and global and regional monitoring
environment. Remote Sensing (RS) data processing
involves a multi-step processing sequence, which includes
independent processing steps depending on the RS
application type. RS data processing for environmental
monitoring and regional disaster is computationally
recognized and has data demand. The combination of
High-Performance Computing and cloud computing
propose an efficient method to solve the problems through
large-scale RS data search processing for several
applications. The elastic, ubiquitous, and high
transparency level of cloud computing enables them to run
massive RS data management and monitor the dynamic
environments on the cloud through the web interface. The
cloud service core provides the parallel file system for
large-scale RS data and as an interface to access RS data
to improve data localization.
Keywords :
Edge Computing; Fog Computing; IoT devices; Multi-faceted and multi-area remote sensing (SAR) datasets; High-Performance Computing; Hilbert-based data indexing.
