@article {Sabri2021888,
	title = {Cloud Computing in Remote Sensing: Big Data Remote Sensing Knowledge Discovery and Information Analysis},
	journal = {International Journal of Advanced Computer Science and Applications},
	volume = {12},
	number = {5},
	year = {2021},
	note = {cited By 1},
	pages = {888-895},
	abstract = {With the rapid development of remote sensing technology, our ability to obtain remote sensing data has been improved to an unprecedented level. We have entered an era of big data. Remote sensing data clear showing the characteristics of Big Data such as hyper spectral, high spatial resolution, and high time resolution, thus, resulting in a significant increase in the volume, variety, velocity and veracity of data. This paper proposes a feature supporting, salable, and efficient data cube for time-series analysis application, and used the spatial feature data and remote sensing data for comparative study of the water cover and vegetation change.The spatial-feature remote sensing data cube (SRSDC) is described in this paper. It is a data cube whose goal is to provide a spatial-feature-supported, efficient, and scalable multidimensional data analysis system to handle largescale RS data. It provides a high-level architectural overview of the SRSDC.The SRSDC offers spatial feature repositories for storing and managing vector feature data, as well as feature translation for converting spatial feature information to query operations.The paper describes the design and implementation of a feature data cube and distributed execution engine in the SRSDC. It uses the long time-series remote sensing production process and analysis as examples to evaluate the performance of a feature data cube and distributed execution engine. Big data has become a strategic highland in the knowledge economy as a new strategic resource for humans. The core knowledge discovery methods include supervised learning methods data analysis supervised learning, unsupervised learning methods data analysis unsupervised learning, and their combinations and variants. {\textcopyright} 2021. All Rights Reserved.},
	keywords = {Big data, Cloud computing, Cloud-computing, Data cube, Data distributed, Data integration, Data mining, Engines, Execution engine, Feature data, Geometry, Methods. Data analysis, Remote sensing, Remote sensing data, Remote sensing technology, Remote-sensing, Spatial features, Supervised learning, Time series analysis, Unsupervised learning},
	doi = {10.14569/IJACSA.2021.01205104},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107802131\&doi=10.14569\%2fIJACSA.2021.01205104\&partnerID=40\&md5=4251cd5707097684639ead5c44f44bec},
	author = {Sabri, Y. and Bahja, F. and Siham, A. and Maizate, A.}
}
