A study on the dynamic evolution method of spatio-temporalfield data based on tensor decomposition

WenJie Xu; Liang Huo; Tao Shen; Su Gao; Zhuang Chen

doi:10.1117/12.2626409

16 December 2021 A study on the dynamic evolution method of spatio-temporalfield data based on tensor decomposition

WenJie Xu, Liang Huo, Tao Shen, Su Gao, Zhuang Chen

Proceedings Volume 12153, International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2021); 1215317 (2021) https://doi.org/10.1117/12.2626409
Event: International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2021), 2021, Sanya, China

Abstract

Spatio-temporal field data is one of the primary forms of digital representation of geographic objects, characterized by massive, multidimensional, complex evolution and analysis-oriented, etc. The organization, management, and analysis of the above data have become important bottlenecks of existing information systems. Moreover, the analysis of dynamic evolution behavior of Spatio-temporal field data is less studied. The correlation between attributes and time is not studied enough, making it difficult to carry out smooth and accurate dynamic simulation and expression in the scene. Therefore, this paper proposes a dynamic organization and description scheme of Spatio-temporal field data based on tensor Tucker decomposition. Firstly, the Spatio-temporal field data is organized based on tensor Tucker decomposition, and the original Spatio-temporal field data is chunked according to time. The Spatio-temporal field data sub-packaging mechanism and dynamic evolution description scheme are designed based on the description document and the field data structure itself; Design time-space field data attributes and the expression relationship of time attributes. Finally, the Spatio-temporal deformation field data model in ground subsidence monitoring is used for applied research. The experimental results show that the dynamic evolution method of Spatio-temporal field data based on tensor decomposition proposed in this paper can achieve accurate dynamic simulation representation of Spatio-temporal field data in geographic scenes.

Citation Download Citation

WenJie Xu, Liang Huo, Tao Shen, Su Gao, and Zhuang Chen "A study on the dynamic evolution method of spatio-temporalfield data based on tensor decomposition", Proc. SPIE 12153, International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2021), 1215317 (16 December 2021); https://doi.org/10.1117/12.2626409

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KEYWORDS

Data modeling

Data storage

Geographic information systems

Data acquisition

Visualization

Analytical research

Matrices

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