Research on urban modern architectural art based on artificial intelligence and GIS image recognition system

被引：10
作者：
Huang W. [1 ]
Ren J. [2 ]
Yang T. [1 ]
Huang Y. [1 ]
机构：
[1] School of Civil Engineering and Architecture, Shaanxi University of Technology, Hanzhong
[2] Collaborative Innovation Center of Biological Resources in Qinba Mountains, Shaanxi University of Technology, No.1 Dongyihuan Road, Hanzhong, 723001, Shaanxi
来源：
Arabian Journal of Geosciences | 2021年 / 14卷 / 10期
关键词：
Artificial intelligence; GIS; Image recognition; Urban architecture;
D O I：
10.1007/s12517-021-07222-z
中图分类号：
学科分类号：
摘要：
At present, the Chinese construction industry has begun to apply artificial intelligence technology to the research of the construction industry, and it has attracted the attention of many researchers. At present, the main application method is in the construction of the internal system of the intelligent building, use the neural network system, the mold control system, the expert system, and the intelligent decision-making system to achieve the goal of the intelligent building service. This article will analyze the overall situation of artificial intelligence applications in intelligent construction at this stage, based on artificial intelligence technology, and study in detail the application of artificial intelligence systems in various aspects of intelligent construction. Re-excavate the connotation of the urban texture with the methods of quantification and data analysis. Take the texture data of multiple cities as an example, and use methods such as image dimensionality reduction, k-means clustering, convolutional neural network classification, and machine learning to build urban images. The identification and evaluation system finally concludes that in the existing evaluation system, the total economic score, the total soft economic score, the environmental score, and the sanitation score have the greatest correlation with the recognition results of urban texture. Among them is the environmental score. The correlation of health scores is in line with the common cognition of urban research scholars, and the calculation results of total economic scores and total soft economic scores indicate that urban architectural planning and its layout are hidden from the economic situation of the city, and also prove artificial intelligence based on image recognition is necessary to mine city information and evaluate it. © 2021, Saudi Society for Geosciences.
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  • [1] Broomhead D., Lowe D., Radial basis functions, multivariable functional interpolation and adaptive networks, Complex Sys, 2, pp. 321-355, (1988)
  • [2] Buyukyildiz M., Tezel G., Yilmaz V., Estimation of the change in lake water level by artificial intelligence methods, Water Resour Manag, 28, 13, pp. 4747-4763, (2014)
  • [3] Chen C.H., Lin Z.S., A committee machine with empirical formulas for permeability prediction, Comput Geosci, 32, pp. 485-496, (2006)
  • [4] Kisi O., Shiri J., Nikoofar B., Forecasting daily lake levels using artificial intelligence approaches, Comput Geosci, 41, pp. 169-180, (2012)
  • [5] Kisi O., Shiri J., Karimi S., Shamshirband S., Motamedi S., Petkovic D., Hashim R., A survey of water level fluctuation predicting in Urmia Lake using support vector machine with firefly algorithm, Appl Math Comput, 270, pp. 731-743, (2015)
  • [6] Labani M.M., Kadkhodaie-Ilkhchi A., Salahshoor K., Estimation of NMR log parameters from conventional well log data using a committee machine with intelligent systems: a case study from the Iranian part of the south pars gas field, Persian Gulf Basin, J Pet Sci Eng, 72, pp. 175-185, (2010)
  • [7] Meng E., Huang S., Huang Q., Fang W., Wu L., Wang L., A robust method for non-stationary streamflow prediction based on improved EMD-SVM model, J Hydrol, 568, pp. 462-478, (2019)
  • [8] Mwale F.D., Adeloye A.J., Rustum R., Application of self-organising maps and multi-layer perceptron artificial neural networks for stream flow and water level forecasting in data-poor catchments: the case of the lower shire floodplain, Malawi, Hydrol Res, 45, 6, pp. 838-854, (2014)
  • [9] Nadiri A.A., Gharekhani M., Khatibi R., Sadeghfam S., Asghari Moghaddam A., Groundwater vulnerability indices conditioned by supervised intelligence committee machine (SICM), Sci Total Environ, 574, pp. 691-706, (2017)
  • [10] Nadiri A.A., Shokri S., T-C Tsai F., Asghari Moghaddam A., Prediction of effluent quality parameters of a wastewater treatment plant using a supervised committee fuzzy logic model, J Clean Prod, 180, pp. 539-549, (2018)
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