ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-2/W1, 25-31, 2013
https://doi.org/10.5194/isprsannals-II-2-W1-25-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
13 Sep 2013
EXPLORING THE ROLE OF GENETIC ALGORITHMS AND ARTIFICIAL NEURAL NETWORKS FOR INTERPOLATION OF ELEVATION IN GEOINFORMATION MODELS
H. Bagheri1, S. Y. Sadjadi1, and S. Sadeghian2 1Dept. of Geomatics Engineering, School of Civil Engineering, University of Tafresh, Tafresh, Iran
2Geomatics College of National Cartographic Centre, Tehran, Iran
Keywords: DEM, DTM, ANN, IDW, AI, GA, Interpolation, Elevations, Optimisation, Height Abstract. One of the most significant tools to study many engineering projects is three-dimensional modelling of the Earth that has many applications in the Geospatial Information System (GIS), e.g. creating Digital Train Modelling (DTM). DTM has numerous applications in the fields of sciences, engineering, design and various project administrations. One of the most significant events in DTM technique is the interpolation of elevation to create a continuous surface. There are several methods for interpolation, which have shown many results due to the environmental conditions and input data. The usual methods of interpolation used in this study along with Genetic Algorithms (GA) have been optimised and consisting of polynomials and the Inverse Distance Weighting (IDW) method. In this paper, the Artificial Intelligent (AI) techniques such as GA and Neural Networks (NN) are used on the samples to optimise the interpolation methods and production of Digital Elevation Model (DEM). The aim of entire interpolation methods is to evaluate the accuracy of interpolation methods. Universal interpolation occurs in the entire neighbouring regions can be suggested for larger regions, which can be divided into smaller regions.

The results obtained from applying GA and ANN individually, will be compared with the typical method of interpolation for creation of elevations. The resulting had performed that AI methods have a high potential in the interpolation of elevations. Using artificial networks algorithms for the interpolation and optimisation based on the IDW method with GA could be estimated the high precise elevations.

Conference paper (PDF, 352 KB)


Citation: Bagheri, H., Sadjadi, S. Y., and Sadeghian, S.: EXPLORING THE ROLE OF GENETIC ALGORITHMS AND ARTIFICIAL NEURAL NETWORKS FOR INTERPOLATION OF ELEVATION IN GEOINFORMATION MODELS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-2/W1, 25-31, https://doi.org/10.5194/isprsannals-II-2-W1-25-2013, 2013.

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