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Study for Multi-Resources Spatial Data Fusion Methods in Big Data Environment

Zhiquan Huanga,b, Yu Fua, Fuchu Daia

a North China University of Water Resources and Electric Power, Zhengzhou, China;
b Xinjiang Institute of Engineering, Urumchi, China

* Corresponding Author: Fuchu Dai, email

Intelligent Automation & Soft Computing 2018, 24(1), 29-34. https://doi.org/10.1080/10798587.2016.1267237

Abstract

The rapid development and extensive application of geographic information system (GIS) and the advent of the age of big data bring about the generation of multi-resources spatial data, which makes data integration and fusion share more difficult due to the differences on data source, data accuracy and data modal. Meanwhile, study for multi-resources spatial data fusion methods has an important practical significance for reducing the production cost of geographic data, accelerating the updating speed of existing geographical information and improving the quality of GIS big data. To expound the formation and developing trends of multi-resources spatial data fusion methods systematically, and on the basis of referring to lots of related technical documents both at home and abroad, this paper makes a conclusion and discussion about multi-resources spatial data fusion methods, and foresees the prospects of data fusion in big data environment, which has certain reference value for the related research work.

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Cite This Article

APA Style
Huang, Z., Fu, Y., Dai, F. (2018). Study for multi-resources spatial data fusion methods in big data environment. Intelligent Automation & Soft Computing, 24(1), 29-34. https://doi.org/10.1080/10798587.2016.1267237
Vancouver Style
Huang Z, Fu Y, Dai F. Study for multi-resources spatial data fusion methods in big data environment. Intell Automat Soft Comput . 2018;24(1):29-34 https://doi.org/10.1080/10798587.2016.1267237
IEEE Style
Z. Huang, Y. Fu, and F. Dai "Study for Multi-Resources Spatial Data Fusion Methods in Big Data Environment," Intell. Automat. Soft Comput. , vol. 24, no. 1, pp. 29-34. 2018. https://doi.org/10.1080/10798587.2016.1267237



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