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Integration of Remote Sensing Data in a Cloud Computing Environment

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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. 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 sensing applications.
Słowa kluczowe
Twórcy
  • Laboratoryl of Innovation in Management and Engineering for Enterprise (LIMIE), ISGA Rabat, 27 Av. Oqba, Agdal, Rabat, Morocco
autor
  • Laboratoryl of Innovation in Management and Engineering for Enterprise (LIMIE), ISGA Rabat, 27 Av. Oqba, Agdal, Rabat, Morocco
autor
  • Laboratoryl of Innovation in Management and Engineering for Enterprise (LIMIE), ISGA Rabat, 27 Av. Oqba, Agdal, Rabat, Morocco
  • Terra Motion Limited, 11 Ingenuity Centre, Innovation Park, Jubilee Campus, University of Nottingham, Nottingham NG7 2TU, UK
Bibliografia
  • [1] Xiang Li and Lingling Wang. On the study of fusion techniques for bad geological remote sensing image. J. Ambient Intelligence and Humanized Computing, 6(1):141-149, 2015. https://doi.org/10.1007/s12652-015-0255-1.
  • [2] Robert Jeansoulin. Review of forty years of technological changes in geomatics toward the big data paradigm. ISPRS International Journal of Geo-Information, 5(9):155, 2016. https://doi.org/10.3390/ijgi5090155.
  • [3] A. Lowe, D.; Mitchell. Status report on NASA’s earth observing data and information system (eosdis). In The 42nd Meeting of the Working Group on Information Systems Services, pages Frascati, Italy, 19-22 September, 2016. https://doi.org/10.5334/dsj-2019-040.
  • [4] China’s fy satellite data center. Available online: http://satellite.cma.gov.cn/portalsite/default.aspx , (accessed on 25 August 2017).
  • [5] China center for resources satellite data and application. Available online: http://www.cresda.com/CN/sjfw/zxsj/index.shtml , (accessed on 25 August 2017).
  • [6] Jining Yan and Lizhe Wang. Suitability evaluation for products generation from multisource remote sensing data. Remote Sensing, 8(12):995, 2016. https://doi.org/10.3390/rs8120995.
  • [7] Minggang Dou, Jingying Chen, Dan Chen, Xiaodao Chen, Ze Deng, Xuguang Zhang, Kai Xu, and Jian Wang. Modeling and simulation for natural disaster contingency planning driven by high-resolution remote sensing images. Future Generation Computer Systems, 37(C):367-377, 2014. https://doi.org/10.1016/j.future.2013.12.018.
  • [8] Dongyao Wu, Liming Zhu, Xiwei Xu, Sherif Sakr, Daniel Sun, and Qinghua Lu. Building pipelines for heterogeneous execution environments for big data processing. IEEE Software, 33(2):60-67, 2016. https://doi.org/10.1109/MS.2016.35.
  • [9] Chaowei Yang, Qunying Huang, Zhenlong Li, Kai Liu, and Fei Hu. Big data and cloud computing: innovation opportunities and challenges. International Journal of Digital Earth, 10(1):13-53, 2017. https://doi.org/10.1080/17538947.2016.1239771.
  • [10] Xin Luo, Maocai Wang, Guangming Dai, and Xiaoyu Chen. A novel technique to compute the revisit time of satellites and its application in remote sensing satellite optimization design. International Journal of Aerospace Engineering,2017,(2017-01-31), 2017(6):1-9, 2017. https://doi.org/10.1155/2017/6469439.
  • [11] Andrew Mitchell, Hampapuram Ramapriyan, and Dawn Lowe. Evolution of web services in eosdis — search and order metadata registry (echo). In Geoscience and Remote Sensing Symposium,2009 IEEE International, igarss, pages 371-374, 2010. https://doi.org/10.1109/IGARSS.2009.5417653.
  • [12] Available online: http://oodt.apache.org/. Oodt. (accessed on 25 January 2017).
  • [13] Chris A. Mattmann, Daniel J. Crichton, Nenad Medvidovic, and Steve Hughes. A software architecture-based framework for highly distributed and data intensive scientific applications. In International Conference on Software Engineering, pages 721-730, 2006.https://doi.org/10.1145/1134285.1134400.
  • [14] Chris A. Mattmann, Dana Freeborn, Dan Crichton, Brian Foster, Andrew Hart, David Woollard, Sean Hardman, Paul Ramirez, Sean Kelly, and Albert Y. Chang. A reusable process control system framework for the orbiting carbon observatory and npp sounder peate missions. In IEEE International Conference on Space Mission Challenges for Information Technology, 2009. Smc-It, pages 165-172, 2009. https://doi.org/10.1109/SMC-IT.2009.27.
  • [15] Dennis C. Reuter, Cathleen M. Richardson, Fernando A. Pellerano, James R. Irons, Richard G. Allen, Martha Anderson, Murzy D. Jhabvala, Allen W. Lunsford, Matthew Montanaro, and Ramsey L. Smith. The thermal infrared sensor (tirs) on Landsat 8: Design overview and prelaunch characterization. Remote Sensing, 7(1):1135-1153, 2015. https://doi.org/10.3390/rs70101135.
  • [16] Khandelwal, Sumit and Goyal, Rohit. Effect of vegetation and urbanization over land surface temperature: case study of Jaipur City. EARSeL Symposium, pages 177-183, 2010.
  • [17] Yaxing Wei, Liping Di, Baohua Zhao, Guangxuan Liao, and Aijun Chen. Transformation of hdf-eos metadata from the ecs model to iso 19115- based xml. Computers & Geosciences, 33(2):238-247, 2007. https://doi.org/10.1016/j.cageo.2006.06.006.
  • [18] Mahaxay, Manithaphone and Arunpraparut, Wanchai and Trisurat, Yongyut and Tangtham, Nipon. Modis: An alternative for updating land use and land cover in large river basin. Thai J. For, 33(3):34-47, 2014.
  • [19] Bo Zhong, Yuhuan Zhang, Tengteng Du, Aixia Yang, Wenbo Lv, and Qinhuo Liu. Cross-calibration of hj-1/ccd over a desert site using Landsat etm + imagery and Aster gdem product. IEEE Transactions on Geoscience & Remote Sensing, 52(11):7247-7263, 2014. https://doi.org/10.1109/TGRS.2014.2310233.
  • [20] Ranjeet Devarakonda, Giriprakash Palanisamy, Bruce E. Wilson, and James M. Green. Mercury: reusable metadata management, data discovery and access system. Earth Science Informatics, 3(1-2):87-94, 2010. https://doi.org/10.1007/s12145-010-0050-7.
  • [21] Nengcheng Chen and Chuli Hu. A sharable and interoperable metamodel for atmospheric satellite sensors and observations. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 5(5):1519-1530, 2012. https://doi.org/10.1109/JSTARS.2012.2198616.
  • [22] Peng Yue, Jianya Gong, and Liping Di. Augmenting geospatial data provenance through metadata tracking in geospatial service chaining. Computers Geosciences, 36(3):270-281, 2010. https://doi.org/10.1016/j.cageo.2009.09.002.
  • [23] Gilman, Jason Arthur and Shum, Dana. Making metadata better with cmr and mmt. NASA, 2016. https://ntrs.nasa.gov/search.jsp?R=20160009277.
  • [24] Ann B. Burgess and Chris A. Mattmann. Automatically classifying and interpreting polar datasets with Apache tika. In IEEE International Conference on Information Reuse and Integration, pages 863-867, 2014, 2014. https://doi.org/10.1109/IRI.2014.7051982.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3b61ea71-99d7-44dc-a85d-3289ef3df8bc
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