A comparative study for the estimation of geodetic point velocity by artificial neural networks

Yılmaz, Mustafa; Güllü, Mevlüt

doi:10.1007/s12040-014-0411-6

Cited by 15 publications

(9 citation statements)

References 58 publications

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“…KRG uses the semivariogram which describes the variability of data points depending on their distance and direction, and nearby values define the weights that determine the contribution of each data point to the prediction of new values at unsampled locations (Krivoruchko and Gotway, 2004). In case the semi variogram is known, KRG constitutes the best linear unbiased estimator (BLUE) (Yilmaz and Gullu, 2014 (Fig. 1).…”

Section: Interpolation Methodsmentioning

confidence: 99%

Comparing Uniform and Random Data Reduction Methods for DTM Accuracy

Yılmaz

Uysal

2017

International Journal of Engineering and Geosciences

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ABSTRACT:The digital cartographic representation of the elevation of the earth's surface created from discrete elevation points is defined as a digital terrain model (DTM). DTMs have been used in a wide range of applications, such as civil planning, flood control, transportation design, navigation, natural hazard risk assessment, hydraulic simulation, visibility analysis of the terrain, topographic change quantification, and forest characterization. Remote sensing, laser scanning, and radar interferometry become efficient sources for constructing high-accuracy DTMs by the developments in data processing technologies. The accuracy, the density, and the spatial distribution of elevation points, the terrain surface characteristics, and the interpolation methods have an influence on the accuracy of DTMs. In this study, uniform and random data reduction methods are compared for DTMs generated from airborne Light Detection and Ranging (LiDAR) data. The airborne LiDAR data set is reduced to subsets by using uniform and random methods, representing the 75%, 50%, and 25% of the original data set. Over the Mount St. Helens in southwest Washington State as the test area, DTM constructed from the original airborne LiDAR data set is compared with DTMs interpolated from reduced data sets by Kriging interpolation method. The results show that uniform data reduction method can be used to reduce the LiDAR datasets to 50% density level while still maintaining the quality of DTM.

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Section: Interpolation Methodsmentioning

confidence: 99%

Comparing Uniform and Random Data Reduction Methods for DTM Accuracy

Yılmaz

Uysal

2017

International Journal of Engineering and Geosciences

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“…Therefore, ANN has a unique superiority over the conventional prediction techniques (i.e. interpolation methods) [21].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…The development and optimisation approach of Yilmaz and Gullu [21] is followed while structuring BPANN. The detailed information can be found in the related reference.…”

Section: Bpann Design and Optimisationmentioning

confidence: 99%

“…In diversified fields of engineering, the artificial neural network (ANN) has been utilized. ANN applications in geodetic, cartographic, and surveying applications have increased in the last decade and remarkable accomplishments were obtained through ANNs such as: deformation analysis [13], outlier detection in geodetic networks [14], ambiguity resolution for GNSS [15], coordinate-datum transformation [16,17], local geoid modelling [18], georeferencing of historical maps [19], prediction of the Earth orientation parameters [20], geodetic velocity estimation [21].…”

Section: Introductionmentioning

confidence: 99%

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Application of artificial neural networks to height transformation

2017

Teh. vjesn.

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Original scientific paperThe vertical positioning has two indispensable constituents: the height and the relevant reference surface. The definition of the height differs according to the appointed reference surface. Global Navigation Satellite Systems (GNSS) ensure ellipsoidal heights relative to a geodetic reference ellipsoid surface. However, many field applications require heights that are related to a physically meaningful surface (e.g. the geoid). Such physically meaningful heights often provided in terms of orthometric heights. The geoid undulation is the relation between the ellipsoidal and orthometric heights. The ellipsoidal heights can be transformed to orthometric heights via two principal approaches: a gravimetric geoid model, and geometrical interpolation between geoid undulations where GNSS observations have been collocated with benchmarks. The purpose of this study is investigating the applicability of a back propagation artificial neural network as a height transformation tool. Keywords: back propagation artificial neural networks; ellipsoidal height; orthometric height Primjena umjetnih neuronskih mreža na transformaciju visineIzvorni znanstveni članak Dvije su osnovne komponente vertikalnog pozicioniranja: visina i odgovarajuća referentna površina. Definicija visine se mijenja u skladu s odabranom referentnom površinom. Satelitski sustavi globalne navigacije (Global Navigation Satellite Systems -GNSS) daju elipsoidne visine razmjerne površini geodetski referentnog elipsoida. Međutim, mnoge aplikacije zahtijevaju visine koje su povezane s fizikalno značajnom površinom kao što je geoid. Takve visine su u obliku ortometrijskih (ili normalnih) visina. Veza između elipsoidnih i ortometrijskih visina je visina geoida iznad referentnog elipsoida, obično nazivana valovitim kretanjem geoida. Dva su osnovna pristupa kod pretvaranja elipsoidnih u ortometrijske visine: gravimetrijski model geoida i interpolacija između geometrijski deriviranih valovitih kretanja geoida gdje su GNSS mjerenja locirana uz nivelacijske točke. U ovom se radu istražuje korisnost umjetnih neuronskih mreža s unatražnim rasprostiranjem kao alternativni alat za transformaciju visine.Ključne riječi: elipsoidna visina; ortometrijska visina; umjetne neuronske mreže s unatražnim rasprostiranjem

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“…This assertion is well documented in literature. For example, ANN has been applied to solve most coordinate transformation problems between global and local datums (Gullu 2010;Gullu et al 2011;Lin and Wang 2006;Mihalache 2012;Tierra et al 2008Tierra et al , 2009Tierra and Romero 2014;Turgut 2010;Yilmaz and Gullu 2012;Zaletnyik 2004), for GPS height conversion (Fu and Liu 2014;Liu et al 2011;Lei and Qi 2010;Tieding et al 2010;Wu et al 2012a), in geodetic deformation modelling (Bao et al 2011;Du et al 2014a, b;Gao et al 2014;Pantazis and Eleni-Georgia 2013;Yilmaz and Gullu 2014;Yilmaz 2013), earth orientation parameters determination (Liao et al 2012;Schuh et al 2002;Yu et al 2015), precise orbital prediction (He-Sheng 2006;Li et al 2014), gravity anomaly estimation (Hajian et al 2011;Hamid and Mohammad 2013;Tierra and De Freitas 2005), geoid determination (Kavzoglu and Saka 2005;Pikridas et al 2011;Stopar et al 2006;Sorkhabi 2015;Veronez et al 2006Veronez et al , 2011, transforming from cartesian coordinates to geodetic coordinates (Civicioglu 2012) and many others.…”

Section: Introductionmentioning

confidence: 99%

Capability of Artificial Neural Network for Forward Conversion of Geodetic Coordinates $$(\phi ,\lambda ,h)$$ ( ϕ , λ , h ) to Cartesian Coordinates (X, Y, Z)

Ziggah

et al. 2016

Math Geosci

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The standard forward transformation equation plays a major role in coordinate transformation between global and local datums. Thus, it is a prerequisite step in the forward conversion of geodetic coordinates into cartesian coordinates in coordinate transformation from global to local datum and vice versa. Numerous studies have been carried out on converting cartesian coordinates to geodetic coordinates (reverse procedure) through the application of iterative, approximate, closed form, vector-based and computational intelligence algorithms. However, based on literature covered pertaining to this study, it was realized that the existing researches do not fully address the issue of applying and testing alternative techniques in the case of the forward conversion. Hence, the purpose of this present study was to explore the coordinate conversion performance of two different artificial neural network approaches (backpropagation artificial neural network (BPANN) and radial basis function neural network (RBFNN)) and multiple linear regression (MLR). The statistical findings revealed that the BPANN, RBFNN and MLR offered satisfactory prediction of cartesian coordinates. However, the RBFNN compared to BPANN and MLR showed better stability and more accurate prediction results. Furthermore, in terms of maximum three-dimensional position error, the RBFNN attained 0.004 m while 0.011 and 0.627 m were achieved, respectively, by MLR and BPANN. By virtue of the success achieved in this study, the main conclusion drawn here is that RBFNN provides a promising alter-B Hu Youjian 123 Math Geosci native in the forward conversion of geodetic coordinates into cartesian coordinates. Therefore, the capability of artificial neural network as a powerful tool for solving majority of function approximation problems in geodesy has been demonstrated.

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A comparative study for the estimation of geodetic point velocity by artificial neural networks

Cited by 15 publications

References 58 publications

Comparing Uniform and Random Data Reduction Methods for DTM Accuracy

Comparing Uniform and Random Data Reduction Methods for DTM Accuracy

Application of artificial neural networks to height transformation

Capability of Artificial Neural Network for Forward Conversion of Geodetic Coordinates $$(\phi ,\lambda ,h)$$ ( ϕ , λ , h ) to Cartesian Coordinates (X, Y, Z)

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