Integration of Remote Sensing Data in a Cloud
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 h...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Polish Academy of Sciences
2022-03-01
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| Series: | International Journal of Electronics and Telecommunications |
| Subjects: | |
| Online Access: | https://journals.pan.pl/Content/122818/PDF/24_3329_Sabri_L_sk.pdf |
| _version_ | 1811314570789126144 |
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| author | Yassine Sabri Fadoua Bahja Henk Pet |
| author_facet | Yassine Sabri Fadoua Bahja Henk Pet |
| author_sort | Yassine Sabri |
| collection | DOAJ |
| description | 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 timeseries analysis application, and used the spatial feature data and remote sensing data for comparative study of the water cover and vegetation change. In this system, the feature data cube building and distributed executor engine are critical in supporting large spatiotemporal RS data analysis with spatial features. The feature translation ensures that the geographic object can be combined with satellite data to build a feature data cube for analysis. Constructing a distributed executed engine based on dask ensures the efficient analysis of large-scale RS data. This work could provide a convenient and efficient multidimensional data services for many remote sens-ing applications. |
| first_indexed | 2024-04-13T11:14:57Z |
| format | Article |
| id | doaj.art-e853e75d41414582993c625725e3097a |
| institution | Directory Open Access Journal |
| issn | 2081-8491 2300-1933 |
| language | English |
| last_indexed | 2024-04-13T11:14:57Z |
| publishDate | 2022-03-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | International Journal of Electronics and Telecommunications |
| spelling | doaj.art-e853e75d41414582993c625725e3097a2022-12-22T02:49:01ZengPolish Academy of SciencesInternational Journal of Electronics and Telecommunications2081-84912300-19332022-03-01vol. 68No 1167172https://doi.org/10.24425/ijet.2022.139864Integration of Remote Sensing Data in a CloudYassine Sabri0Fadoua Bahja1Henk Pet2Laboratory of Innovation in Management and Engineering for Enterprise (LIMIE), ISGA Rabat, 27 Avenuel Oqba, Agdal, Rabat, MoroccoLaboratory of Innovation in Management and Engineering for Enterprise (LIMIE), ISGA Rabat, 27 Avenuel Oqba, Agdal, Rabat, MoroccoTerra Motion Limited, 11 Ingenuity Centre, Innovation Park, Jubilee Campus, University of Nottingham, Nottingham NG7 2TU, UKWith 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 timeseries analysis application, and used the spatial feature data and remote sensing data for comparative study of the water cover and vegetation change. In this system, the feature data cube building and distributed executor engine are critical in supporting large spatiotemporal RS data analysis with spatial features. The feature translation ensures that the geographic object can be combined with satellite data to build a feature data cube for analysis. Constructing a distributed executed engine based on dask ensures the efficient analysis of large-scale RS data. This work could provide a convenient and efficient multidimensional data services for many remote sens-ing applications.https://journals.pan.pl/Content/122818/PDF/24_3329_Sabri_L_sk.pdfremote sensingdata integrationcloud computingbig data |
| spellingShingle | Yassine Sabri Fadoua Bahja Henk Pet Integration of Remote Sensing Data in a Cloud International Journal of Electronics and Telecommunications remote sensing data integration cloud computing big data |
| title | Integration of Remote Sensing Data in a Cloud |
| title_full | Integration of Remote Sensing Data in a Cloud |
| title_fullStr | Integration of Remote Sensing Data in a Cloud |
| title_full_unstemmed | Integration of Remote Sensing Data in a Cloud |
| title_short | Integration of Remote Sensing Data in a Cloud |
| title_sort | integration of remote sensing data in a cloud |
| topic | remote sensing data integration cloud computing big data |
| url | https://journals.pan.pl/Content/122818/PDF/24_3329_Sabri_L_sk.pdf |
| work_keys_str_mv | AT yassinesabri integrationofremotesensingdatainacloud AT fadouabahja integrationofremotesensingdatainacloud AT henkpet integrationofremotesensingdatainacloud |