Datum Transformation by Artificial Neural Networks for Geographic Information Systems Applications

Güllü, Mevlüt; Yılmaz, Mustafa; Yılmaz, İbrahim; Turgut, Bayram

doi:10.5053/isepp.2011.1-6

Cited by 12 publications

(11 citation statements)

References 16 publications

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“…In practice, the most common alternatives could be categorised into artificial intelligence technology (Zaletnyik, 2004;Lin and Wang, 2006;Tierra et al, 2008;Tierra et al, 2009;Gullu, 2010;Turgut, 2010;Gullu et al, 2011;Yilmaz and Gullu, 2012;Mihalache, 2012;Tierra and Romero, 2014), partitioning methods (Lippus, 2004;Kheloufi, 2006), Ill-posed approach Zeng and Yi, 2010;Ge and Wu, 2012), least squares algorithms (Felus and Schaffrin, 2005;Acar et al, 2006aAcar et al, , 2006bRey-Jer and Hwa-Wei, 2006;Akyilmaz, 2007;Janicka, 2011;Mahboub, 2012), quaternions approach (Shen et al, 2006;Jitka, 2011;Zeng and Yi, 2011), dynamic datum transformation approach (Soler, 1998;Soler and Marshall, 2003;Soler and Snay, 2003;Stanaway, 2008;Haasdyk and Janssen, 2011), Procrustes algorithm (Grafarend and Awange, 2003;Zeng, 2015) to mention but a few. It is important to note that coordinate transformation has been chiefly dominated by the above mentioned methods due to their achievable accuracy.…”

Section: Introductionmentioning

confidence: 99%

Novel Approach to Improve Geocentric Translation Model Performance Using Artificial Neural Network Technology

Ziggah

Laari

et al. 2017

Bol. Ciênc. Geod.

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Abstract:Geocentric translation model (GTM) in recent times has not gained much popularity in coordinate transformation research due to its attainable accuracy. Accurate transformation of coordinate is a major goal and essential procedure for the solution of a number of important geodetic problems. Therefore, motivated by the successful application of Artificial Intelligence techniques in geodesy, this study developed, tested and compared a novel technique capable of improving the accuracy of GTM. First, GTM based on official parameters (OP) and new parameters determined using the arithmetic mean (AM) were applied to transform coordinate from global WGS84 datum to local Accra datum. On the basis of the results, the new parameters (AM) attained a maximum horizontal position error of 1.99 m compared to the 2.75 m attained by OP. In line with this, artificial neural network technology of backpropagation neural network (BPNN), radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) were then used to compensate for the GTM generated errors based on AM parameters to obtain a new coordinate transformation model. The new implemented models offered significant improvement in the horizontal position error from 1.99 m to 0.93 m.

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Section: Introductionmentioning

confidence: 99%

Novel Approach to Improve Geocentric Translation Model Performance Using Artificial Neural Network Technology

Ziggah

Laari

et al. 2017

Bol. Ciênc. Geod.

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“…In recent years, artificial neural networks (ANNs) have gained popularity in geodetic sciences community. ANNs have been used for coordinate transformation (Zaletnyik, 2004;Maria, 2012;Tierra, et al, 2008;Gullu, 2010;Weiwei and Xiudong, 2010;Gullu et al, 2011;Tierra and Romero, 2014;Ziggah et al, 2016), geoid determination (Kavzoglu and Saka, 2005;Pikridas, et al, 2011;Memarian Sorkhabi, 2015), geodetic deformation modelling (Bao et al, 2011;Chen and Zeng, 2013;Du et al, 2014;Gao et al, 2014), image and signal processing (Ibrahim, 2010;ALAllaf, 2012;Hai and Thuy, 2012) and determination of earth orientation parameters (Schuh, 2002;Liao, 2012). In this study, the feed forward back propagation (FFBP), cascade forward back propagation (CFBP) and radial basis function neural network (RBFNN) were used as alternative coordinate transformation methods to conduct 2D transformation between the ED50 and ITRF96 systems.…”

Section: Introductionmentioning

confidence: 99%

2d Coordinate Transformation Using Artificial Neural Networks

Konakoǧlu¹,

Çakir²,

Gökalp³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Two coordinate systems used in Turkey, namely the ED50 (European Datum 1950) and ITRF96 (International Terrestrial Reference Frame 1996) coordinate systems. In most cases, it is necessary to conduct transformation from one coordinate system to another. The artificial neural network (ANN) is a new method for coordinate transformation. One of the biggest advantages of the ANN is that it can determine the relationship between two coordinate systems without a mathematical model. The aim of this study was to investigate the performances of three different ANN models (Feed Forward Back Propagation (FFBP), Cascade Forward Back Propagation (CFBP) and Radial Basis Function Neural Network (RBFNN)) with regard to 2D coordinate transformation. To do this, three data sets were used for the same study area, the city of Trabzon. The coordinates of data sets were measured in the ED50 and ITRF96 coordinate systems by using RTK-GPS technique. Performance of each transformation method was investigated by using the coordinate differences between the known and estimated coordinates. The results showed that the ANN algorithms can be used for 2D coordinate transformation in cases where optimum model parameters are selected.

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“…This assertion is well archived in existing technical papers of mathematical geodesy. For example, ANN has been applied to solve most coordinate transformation problems global and local datums [1,9,[21][22][23][24][25][26][27][28][29], and transforming from geodetic coordinates to Cartesian coordinates [2] and many more. ANNs are been criticized for its long training process in achieving the optimal network's topology, and it is not easy to identify the relative importance of potential input variables, and certain interpretive difficulties [30,31].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Multivariate Adaptive Regression Splines (MARS) and Multiple Linear Regression (MLR) for Forward Conversion of Geodetic Coordinates (ϕ, λ, h) to Cartesian Coordinates (X, Y, Z)

Peprah¹,

Mensah²,

Akresi³

2017

JGG

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In Ghana's local Geodetic Reference Network, the standard forward transformation equation has played a major role in coordinate transformation between World Geodetic System 1984 (WGS84) and local geodetic datum. Thus, it is an initial step in forward conversion of geodetic coordinates (ϕ , λ, h) to Cartesian coordinates (X, Y, Z) in transformation from global to local datum and vice versa. Several studies in the recent decades have been conducted on converting Cartesian coordinates to geodetic coordinates (reverse procedure) through the utilisation of iterative, approximate, closed form, vector-based and computational intelligence algorithms. However, based on the existing literature covered pertaining to this present study, it was found that the existing knowledge do not fully adhere to the issue of evaluating alternative techniques in the case of the forward conversion. Hence, the aim of this present study was to explore the coordinate conversion performance of the Multivariate Adaptive Regression Splines ( ). The statistical findings revealed that the MARS and MLR offered satisfactory prediction of Cartesian coordinates. However, the MLR compared to MARS showed better stability and more accurate prediction results. From the results of this present study, the main conclusion drawn is that, MLR provides a promising alternative in the forward conversion of geodetic coordinates into Cartesian coordinates. Therefore, the capability of MLR as a powerful tool for solving majority of function approximation problems in mathematical geodesy has been demonstrated in this present study.

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Datum Transformation by Artificial Neural Networks for Geographic Information Systems Applications

Cited by 12 publications

References 16 publications

Novel Approach to Improve Geocentric Translation Model Performance Using Artificial Neural Network Technology

Novel Approach to Improve Geocentric Translation Model Performance Using Artificial Neural Network Technology

2d Coordinate Transformation Using Artificial Neural Networks

Performance Evaluation of Multivariate Adaptive Regression Splines (MARS) and Multiple Linear Regression (MLR) for Forward Conversion of Geodetic Coordinates (ϕ, λ, h) to Cartesian Coordinates (X, Y, Z)

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