Title: Construction of an urban multi variable geospatial information integration model based on distributed artificial intelligence

Authors: Lede Niu; Mei Pan; Yan Zhou

Addresses: School of Tourism and Geographical Sciences of Yunnan Normal University, Kunming 650500, China ' School of Tourism and Geographical Sciences of Yunnan Normal University, Kunming 650500, China ' School of Tourism and Geographical Sciences of Yunnan Normal University, Kunming 650500, China

Abstract: In order to improve the ability of urban multi-spatial information integration, a multi-spatial information integration model based on artificial intelligence is proposed. The empirical analysis model of urban multivariate geospatial information integration is established, and the statistical characteristics are analysed according to the information distribution. The multi-resolution clustering model of urban multi-spatial information is established by using the multi-regression parameter method. The methods of fuzzy feature extraction and associated feature mining were used to extract information features, and the constraint parameters of urban multivariate geospatial information integration were analysed by GIS parameter estimation and statistical analysis. The parameter optimisation model of urban multivariate geospatial information integration is established and the artificial intelligence learning algorithm is used to integrate urban multivariate geospatial information. The simulation results show that this method has a good adaptability to the integration of urban multi-spatial information, and is helpful to the spatial distribution planning of geographic information, and improves the ability of accurate mining and scheduling of urban multi-spatial information.

Keywords: artificial intelligence; urban; multivariate geo-space; information integration; geographic information system; GIS; fuzzy model.

DOI: 10.1504/IJITM.2022.121333

International Journal of Information Technology and Management, 2022 Vol.21 No.1, pp.1 - 12

Received: 21 Dec 2019
Accepted: 12 May 2020
Published online: 07 Mar 2022 *

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