Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Journal of Applied Geodesy

Editor-in-Chief: Kahmen, Heribert / Rizos, Chris

CiteScore 2018: 1.61

SCImago Journal Rank (SJR) 2018: 0.532
Source Normalized Impact per Paper (SNIP) 2018: 1.064

See all formats and pricing
More options …
Volume 13, Issue 3


Using direct transformation approach as an alternative technique to fuse global digital elevation models with GPS/levelling measurements in Egypt

Hossam Talaat Elshambaky
  • Corresponding author
  • Civil Dept., Misr Higher Institute for Engineering and Technology in Mansoura, Mansoura, Egypt
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2019-03-05 | DOI: https://doi.org/10.1515/jag-2018-0050


Open global digital elevation models (GDEMs) represent a free and important source of information that is available to any country. Fusion processing between global and national digital elevation models is neither easy nor inexpensive. Hence, an alternative solution to fuse a GDEM (GTOPO30 or SRTM 1) with national GPS/levelling measurements is adopted. Herein, a transformation process between the GDEMs and national GPS/levelling measurements is applied using parametric and non-parametric equations. Two solutions are implemented before and after the filtration of raw data from outliers to assess the ability of the generated corrector surface model to absorb the effect of the outliers’ existence. In addition, a reliability analysis is conducted to select the most suitable transformation technique. We found that when both the fitting and prediction properties have equal priority, least-squares collocation integrated with a least-squares support vector machine inherited with a linear or polynomial kernel function exhibits the most accurate behavior. For the GTOPO30 model, before filtration of the raw data, there is an improvement in the mean and root mean square of errors by 39.31 % and 68.67 %, respectively. For the SRTM 1 model, the improvement in mean and root mean square values reached 86.88 % and 75.55 %, respectively. Subsequently, after the filtration process, these values became 3.48 % and 36.53 % for GTOPO30 and 85.18 % and 47.90 % for SRTM 1. Furthermore, it is found that using a suitable mathematical transformation technique can help increase the precision of classic GDEMs, such as GTOPO30, making them to be equal or more accurate than newer models, such as SRTM 1, which are supported by more advanced technologies. This can help overcome the limitation of shortage of technology or restricted data, particularly in developed countries. Henceforth, the proposed direct transformation technique represents an alternative faster and more economical way to utilize unfiltered measurements of GDEMs to estimate national digital elevations in areas with limited data.

Keywords: Least-Squares Collocation; Least-Squares Support Vector Machines; Artificial Neural Network; Global Digital Elevation Models; Support Vector Machines; Bursa–Wolf; Molodensky; SRTM; GTOPO30


  • [1]

    Abd-Elmotaal, H. A., (1994), “Comparison of polynomial and similarity transformation-based datum-shifts for Egypt”, Bulletin Geodesique, vol. 68, pp. 168–172.CrossrefGoogle Scholar

  • [2]

    Abd-Elmotaal, H. A., (2011), “The new Egyptian height models EGH10”, NRIAG journal of astronomy and geophysics, Special issue, pp. 249–261.Google Scholar

  • [3]

    Aguilar, F. J., Agüera, F., Aguilar, M. A., and Carvajal, F., (2005), “Effects of terrain morphology, sampling density, and interpolation methods on grid DEM accuracy”, Photogrammetric Engineering and Remote Sensing, vol. 71, no. 7, pp. 805–816, doi: .CrossrefGoogle Scholar

  • [4]

    Aguilar, F. J., Aguilar, M. A., and Agüera, F., (2007), “Accuracy assessment of digital elevation models using a non-parametric approach”, International Journal of Geographic Information Science, vol. 21, no. 6, pp. 667–686, doi: .CrossrefGoogle Scholar

  • [5]

    Akyilmaz, O., Özlüdemir, M. T., Ayan, T., and Çelik, R. N., (2009), “Soft computing methods for geoidal height transformation”, Earth Planets and Space, vol. 61, no. 7, pp. 825–833.CrossrefGoogle Scholar

  • [6]

    Ali, M. E. O., Shaker, I. F. M., and Saba, N. M., (2017), “A perspective of reliable and accurate DEM using world DEMs data fusion”, ISER, vol. 8, no. 6. ISSN 2229-5518.Google Scholar

  • [7]

    Ali, M. H. and Abustan, I., (2014), “A new novel index for evaluating model performance”, Journal of Natural Resources and Development, vol. 2014, no. 04, pp. 1–9, doi: .CrossrefGoogle Scholar

  • [8]

    Al-Karargy, E. M., Hosny, M. M., and Dawod, G. M., (2015), “Investigation the precision of recent global geoid models and global digital elevation models for geoid modeling in Egypt”, Regional Conference on Surveying and Development, Sharm El-Sheikh, Egypt, 3–6 October 2015.Google Scholar

  • [9]

    Al-Krargy, E. M., Doma, M. I., and Dawod, G. M., (2014), “Towards an Accurate Definition of the Local Geoid Model in Egypt using GPS/Leveling Data: A Case Study at Rosetta Zone”, International Journal of Innovative Science and Modern Engineering (IJISME), vol. 2, no. 11. ISSN: 2319-6386.Google Scholar

  • [10]

    Amidror, I., (2002), “Scattered data interpolation methods for electronic imaging systems: a survey”, Journal of Electronic Imaging, vol. 11, no. 2, pp. 157–176.CrossrefGoogle Scholar

  • [11]

    Amin, M. M., El-fatraiy, S. M., and Saba, N. M., (2013), “Accuracy assessment of world DEMs versus local DEM in Egypt”, Civil Engineering Research Magazine CERM, vol. 35, no. 3. Published by Faculty of Engineering, Al-Azhar University, Cairo, Egypt.Google Scholar

  • [12]

    Arabelos, A. and Tziavos, I. N., (1983), “Determination of Deflection of the vertical using a combination of spherical harmonics and gravimetric data for the area of Greece”, Bull. Géod., vol. 57, pp. 240–256.CrossrefGoogle Scholar

  • [13]

    Arabelos, D., (2000), “Inter comparisons of the global DTMs ETOPO5, Terrain Base and JGP95E”, Physics and Chemistry of the Earth, Part A: Solid Earth and Geodesy, vol. 25, no. 1, pp. 89–93.CrossrefGoogle Scholar

  • [14]

    Beale, M. H., Hagan, M. T., and Demuth, H. B., (2015), “Neural network toolbox user’s guide”, The Math Works, Inc.Google Scholar

  • [15]

    Böhm, W., Farin, G., and Kahmann, J., (1984), “A survey of curve and surface methods in CAGD’’, Comput. Aided Des., vol. 1, pp. 1–60.Google Scholar

  • [16]

    Cakir, L. and Yilmaz, N., (2014), “Polynomial, radial basis functions and multilayer perceptron neural network methods in local geoid determination with GPS/levelling”, J. Measurements, vol. 57, pp. 48–153.Google Scholar

  • [17]

    Cross, P. A., (1983), “Advanced least squares applied to position fixing”, North East London Polytechnic Department of Land Surveying.Google Scholar

  • [18]

    Darbeheshti, N. and Featherstone, W. E., (2009), “Non-stationary covariance function modeling in 2D least-squares collocation”, J. Geod., vol. 83, pp. 495–508, doi: .CrossrefGoogle Scholar

  • [19]

    Dawod, G., (2013), “Suitability analysis for tourist infrastructures utilizing multi-criteria GIS: A case study in Al-Hada city, Saudi Arabia”, International journal of geomatics and geosciences, vol. 4, no. 2, pp. 313–324.Google Scholar

  • [20]

    Dawod, G. and Al-Ghamdy, K., (2017), “Reliability of recent global digital elevation models for geomatics application in Egypt and Saudi Arabia”, Journal of Geographic Information System, vol. 9, pp. 685–698, doi: .CrossrefGoogle Scholar

  • [21]

    Dawod, G. and Mandoer, M. S., (2016), “Optimum Sites for Solar Energy Harvesting in Egypt Based on Multi-Criteria GIS”, The 11th The First Future University International Conference on New Energy and Environmental Engineering Cairo, Egypt. April 11–14, 2016.Google Scholar

  • [22]

    De Brabanter, K., Karsmakers, F., Ojeda, C., Alzate, J., De Brabanter, J., Pelckmans, K., De Moor, B., Vandewalle, J., and Suykens, J. A. K., (2011), “LS-SVMlab Toolbox User’s Guide Version 1.8”, ESAT-SISTA Technical report 10-146, Katholieke Universiteit Leuven, Belgium, http://www.esat.kuleuven.be/_sista/lssvmlab/.Google Scholar

  • [23]

    Deakin, R. E., (2006), “A Note on the Bursa-Wolf and Molodensky-Badekas Transformations”, School of mathematical and geospatial sciences, RMIT University, Australia.Google Scholar

  • [24]

    Denker, H., (2005), “Evaluation of SRTM3 and GTOPO30 Terrain Data in Germany”. In: Jekeli C., Bastos L., Fernandes J. (eds.) Gravity, Geoid and Space Missions. International Association of Geodesy Symposia, vol. 129, pp. 218–223, Springer, Berlin, Heidelberg, doi: .CrossrefGoogle Scholar

  • [25]

    Dermanis, A., (1984), “Kriging and Collocation – A comparison”, Manuscript Geodaetica, vol. 9, pp. 159–167.Google Scholar

  • [26]

    Doganalp S., (2016), “Geoid height computation in strip-area project by using least-squares collocation”, Acta Geodyn. Geomater., vol. 13, no. 2 (182), pp. 167–176, doi: .CrossrefGoogle Scholar

  • [27]

    Ebaid, H., (2014), “Accuracy enhancement of SRTM and ASTER DEMs using weight estimation regression model”, IJRET, vol. 3, no. 08, ISSN: 2319-1163 (online), ISSN: 2321-7308 (print).Google Scholar

  • [28]

    El-quilish, M., El-ashquer M., Dawod, G, and El fiky, G., (2018), “Development and accuracy assessment of high-resolution digital elevation model using GIS approaches for the nile delat region, Egypt”, American Journal of Geographic Information System, vol. 7, no. 4, pp. 107–117, doi: .CrossrefGoogle Scholar

  • [29]

    Elshambaky, H. T., (2017), “Application of neural network to determine a corrector surface for global geopotential model using GPS/levelling measurements in Egypt”, J. Appl. Geodesy, vol. 12, no. 1, pp. 29–44, doi: .CrossrefGoogle Scholar

  • [30]

    Elshambaky, H. T., (2018), “Enhancing the predictability of least-squares collocation through the integration with least-squares-support vector machine”, J. Appl. Geodesy, doi: .CrossrefGoogle Scholar

  • [31]

    Elshambaky, H. T., Kaloop, M. R., and Hu, J. W., (2018), “A novel three-direction datum transformation of geodetic coordinates for Egypt using artificial neural network approach”, Arabian Journal of Geoscience, vol. 11, pp. 110, doi: .CrossrefGoogle Scholar

  • [32]

    El shouny, A., Al-karagy, E. M., Mohamed, H. F., and Dawod, G. M., (2018), “GIs-based accuracy assessment of global geopotential models: a case study of Egypt”, American Journal of Geographic Information System, vol. 7, no. 4, pp. 118–124, doi: .CrossrefGoogle Scholar

  • [33]

    Espinoza, M., Suykens J. A. K., and De Moor, B., (2005), “Load forecasting using fixed-size least squares support vector machines”. In: Cabestany, J., Prieto, A., Sandoval, F. (eds.) “Computational Intelligence and Bioinspired Systems”, IWANN 2005. Lecture notes in computer science, vol. 3512, Springer, Berlin, Heidelberg, doi: .CrossrefGoogle Scholar

  • [34]

    Fan, R. E., Chen, P. H., and Lin, C. J., (2005), “Working set selection using second order information for training support vector machines”, Journal of Machine Learning Research, vol. 6, pp. 1871–1918.Google Scholar

  • [35]

    Fan, R. E., Chen, P. H., and Lin, C. J., (2006), “A study on SMO-type decomposition methods for support vector machines”, IEEE Transactions on Neural Networks, vol. 17, pp. 893–908.CrossrefGoogle Scholar

  • [36]

    Farin, G., (1997), “Curves and Surfaces for Computer Aided Geometric Design”, 4th edn., Academic, San Diego.Google Scholar

  • [37]

    Fazilova, D., (2017), “The review and development of a modern GNSS network and datum in Uzbekistan”, Geodesy and Geodynamics, pp. 2–7.Google Scholar

  • [38]

    Fisher, P. F. and Tate, N. J., (2006), “Causes and consequences of error in digital elevation models”, Progress in Physical Geography, vol. 30, no. 4, pp. 467–489, doi: .CrossrefGoogle Scholar

  • [39]

    Florinsky, I. V., (2012), “Digital terrain analysis in soil science and geology”, Academic Press.Google Scholar

  • [40]

    Fotopoulos, G., Featherstone, W. E., and Sideris, M. G., (2002), “Fitting a gravimetric geoid model to the Australian height datum via GPS data”, IAG Third Meeting of the International Gravity and Geoid Commission, Thessaloniki, Greece, Aug. 26–30, 2002.Google Scholar

  • [41]

    Fotopoulos, G., Kotsakis, C., and Sideris, G., (2003), “How accurately can we determine Orthometric height differences from GPS and geoid data”, J. Surv. Eng., vol. 129, no. 1, pp. 1–10, doi: .CrossrefGoogle Scholar

  • [42]

    Gad, M. A., Odalović, O. R., and Zaky, K. M., (2018), “Case study – Accuracy assessment of SRTM 1,3-arcsec by using topographic DEM over limited area of Egypt territory”, IJSER, vol. 9, no. 8.Google Scholar

  • [43]

    Gesch, D. B., (1998), “Accuracy assessment of a global elevation model using shuttle laser altimeter data”, IGARSS ’98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No. 98CH36174), Seattle, USA, doi: .CrossrefGoogle Scholar

  • [44]

    Gesch, D. B. and Larson, K. S., (1996), “Techniques for development of global 1-kilometer digital elevation models”, in Proceedings, Pecora Thirteen Symposium, Sioux Falls, South Dakota, August 20–22,1996 (CD-ROM), Am. Soc. for Photogrammetry and Remote Sens., Bethesda, Md., 1998.

  • [45]

    Gesch, D. B., Verdin, K. L., and Greenlee, S. K., (1999). New land surface digital elevation model covers the Earth. Eos, Transactions American Geophysical Union, vol. 80, no. 6, pp. 69–70.CrossrefGoogle Scholar

  • [46]

    Grohmann, C. H., (2016), “Comparative analysis of global digital elevation models and ultra-prominent mountain peaks”, ISPRS Annals of the photogrammetry, Remote Sensing and Spatial Information Science, vol. III-4, 2016, XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic.Google Scholar

  • [47]

    Hagan, M. T., Demuth, H. B., and Beale, M. H., (1996), “Neural Network Design”, Boston, MA PWS Publishing.Google Scholar

  • [48]

    Hardin, D. J., Gesch, D. B., Carabajal, C. C., and Luthcke, S. B., (1998), “Application of the shuttle laser altimeter in an accuracy assessment of GTOPO30, a global 1-kilometer digital elevation model”, ISPRS Mapping surface structure and topography by airborne and spaceborne lasers, vol. WG III-5, XXXII-3/W14 ISPRS Congress, 9–11 Nov. 1999, La Jolla, USA, http://www.isprs.org/PROCEEDINGS/XXXII/3-W14/default.aspx.Google Scholar

  • [49]

    Haykin, S., (2001), “Neural Network: A Comprehensive Foundation”, 2nd edn., Hamilton, Ontario, Canada.Google Scholar

  • [50]

    Hengl, T. and Evans, I. S., (2009), “Mathematical and digital models of the land surface”, Geomorphometry concepts, software, applications, T. Hengl and H. I. Reuter, pp. 31–63.Google Scholar

  • [51]

    Hilton, R. D., Featherstone, W. E., Berry, P. A. M., Johnson, C. P. D., and Kirby, J. F., (2003), “Comparison of digital elevation models over Australia and external validation using ERS-1 satellite radar altimetry”, Australian Journal of Earth Science, vol. 50, pp. 157–168, doi: .CrossrefGoogle Scholar

  • [52]

    Hornik, K. M., Stinchcombe, M., and White, H., (1989), “Multilayer feedforward Networks are universal approximators”, Neural Networks, vol. 2, no. 5, pp. 359–366.CrossrefGoogle Scholar

  • [53]

    James, G., Witten, D., Hastie, T., and Tibshirani, R., (2013), “An introduction to statistical learning: with application in R”, Springer Texts in statistics, vol. 103, doi: .CrossrefGoogle Scholar

  • [54]

    Jordan, S. K., (1972), “Self-consistent statistical models for the gravity anomaly, vertical deflection, and undulation of the geoid”, J. Geophys. Res., vol. 77, no. 20, pp. 3660–3670.CrossrefGoogle Scholar

  • [55]

    JPL-Shuttle Radar Topography Mission, (2015), “U.S. Releases Enhanced Shuttle Land Elevation Data” https://www2.jpl.nasa.gov/srtm/.Google Scholar

  • [56]

    Kahaner, D., Cleve, M., and Stephen, N., (1988), “Numerical Methods and Software”, Upper Saddle River, NJ: Prentice Hall.Google Scholar

  • [57]

    Kimehr, R. and Sjöberg, L. E., (2005), “Effect of the SRTM global DEM on the determination of high-resolution geoid model: a case study in Iran”, J. Geod., vol. 2005, no. 79, pp. 540–551, doi: .CrossrefGoogle Scholar

  • [58]

    Kotsakis, C. and Sideris, M. G., (1999), “On the adjustment of combined GPS/levelling/geoid networks”, Journal of Geodesy, vol. 73, pp. 412–421, doi: .CrossrefGoogle Scholar

  • [59]

    Kreyszig, E., Kreyszig, H. and Norminton, E. J., (2011), “Advanced engineering mathematics”, 10th edn., John Wiley & Sons, Inc., USA.Google Scholar

  • [60]

    Mataija, M., Pogarčic, M., and Pogarčic, I., (2014), “Helmert transformation of reference coordinating systems for geodesic purposes in local frames”, Procedia Engineering, vol. 69, pp. 168–176.CrossrefGoogle Scholar

  • [61]

    Math Works Inc., (2015), “Curve fitting toolbox user’s guide”, R2018b, Math Work Inc., pp. 6-2–6-47.

  • [62]

    Maune, D. F., (2007), “Digital Elevation Model Technologies and Applications, the DEM User’s Manual”, Bethesda, MD: American Society for Photogrammetry and Remote Sensing.Google Scholar

  • [63]

    Mikhail, E. M. and Ackermann, F., (1976), “Observations and Least Squares”. Dun Donnelly, New York.Google Scholar

  • [64]

    Milton, J. S. and Arnold, J. C., (1995), “Introduction to Probability and Statistics Principals and Applications for Engineering and the Computing Science”, 3rd edn., McGraw–Hill Book Company, New York, USA.Google Scholar

  • [65]

    Mohamed, M. H. and Saleh, S. S., (2018), “Fusion of SRTM and ASTER GDEM2 DEMs based on height error weighted average technique”, AJBAS, vol. 12, no. 6, pp. 23–29, doi: .CrossrefGoogle Scholar

  • [66]

    Moore, T. and Smith, M. J., (1998), “Back to basics geodetic transformations”, The University of Nottingham. Survey Review, vol. 34, p. 270.Google Scholar

  • [67]

    Moritz, H., (1978), “Least – Squares Collocation”, Review of Geophysics and Space Physics, vol. 16, pp. 421–430.CrossrefGoogle Scholar

  • [68]

    Moritz, H., (1980), “Advanced physical geodesy”, Abacus, Tunbridge Wells Kent.Google Scholar

  • [69]

    Platt, J., (1998), “Sequential minimal optimization: A fast algorithm for training support vector machines”, Technical report, MSR-TR-98-14.Google Scholar

  • [70]

    Powell, S. M. (1997). “Results of the Final Adjustment of the New National Geodetic Network”, Technical report, Egyptian Surveying Authority, Egypt.Google Scholar

  • [71]

    Rabah, M., El-Hattab, A., and Abdallah, M., (2017), “Assessment of the most recent satellite based digital elevation models of Egypt”, NRIAG journal of astronomy and geophysics, vol. 6, no. 2017, pp. 326–335, doi: .CrossrefGoogle Scholar

  • [72]

    Rodriguez, E., Morris, C. S., Belz, J. E., (2006), “A global assessment of the SRTM performance”, Photogrammetric Engineering and Remote Sensing, vol. 72, no. 3, pp. 249–260. doi: .CrossrefGoogle Scholar

  • [73]

    Rodriguez, E., Morris, C. S., Belz, J. E., Chapin, E. C., Martin, J. M., Daffer, W., and Hensley, S. (2005), “An assessment of the SRTM topographic products”, JPL Publ., D31639.Google Scholar

  • [74]

    Samui, P., Kim, D., and Aiyer, B. G., (2015), “Pullout capacity of small ground anchor: a least square support vector machine approach”, Journal of Zhejiang University-Science A (Applied Physics & Engineering), ISSN 1673-565X (print), ISSN 1862-1775 (online), www.zju.edu.cn/jzus; www.springerlink.com.Google Scholar

  • [75]

    Schumaker, L. L., (1982), “Fitting surfaces to scattered data”, No. 19830007490, Conference Paper, Proc. of the NASA Workshop on Surface Fitting, pp. 27–94.Google Scholar

  • [76]

    Schwarz, K. P. and Lachapelle, G., (1980), “Local characteristics of the gravity anomaly covariance function”, Bull. Géod., vol. 54, pp. 21–36.CrossrefGoogle Scholar

  • [77]

    Shen, Y. Z., Chen, Y., and Zheng, D. H., (2006), “A quaternion-based geodetic datum transformation algorithm”, J Geod., vol. 80, no. 5, pp. 233–239.CrossrefGoogle Scholar

  • [78]

    Shuanggen, J., (2012), “Global navigation satellite system: Signals, Theory, and Application”, ISBN 978-953-307-843-4, In Tech Europe, University Campus SteP Ri, Slavaka Krautzeka 83/A, 51000 Rijeka, Croatia, www.intechopen.com.Google Scholar

  • [79]

    Stopar, B., Ambrozic, T., Kuhar, M., and Turk, G., (2006), “GPS-derived geoid using artificial neural network and least squares collocation”, Survey Review, vol. 38, p. 300.Google Scholar

  • [80]

    Suykens J. A. K., (2001), “Support vector machines: A nonlinear modelling and control perspective”, European Journal of Control, vol. 7, pp. 311–327.CrossrefGoogle Scholar

  • [81]

    Suykens J. A. K. and Vandewalle J., (1999), “Least squares support vector machine classifiers”, Neural Processing Letters, vol. 9, no. 3, pp. 293–300.CrossrefGoogle Scholar

  • [82]

    Suykens J. A. K., Van Gestel T., De Brabanter J., De Moor, B., and Vandewalle, J., (2002), “Least Squares Support Vector Machines”, World Scientific, Singapore.Google Scholar

  • [83]

    Szu-Pyng, K., Chao-Nan, C., Hui-Chi, H., Yu-Ting, S., (2014), “Using a least squares support vector machine to estimate a local geoid model”, Bol. Ciênc. Geod., vol. 20, no. 2, doi: .CrossrefGoogle Scholar

  • [84]

    Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D., (2007), “The Shuttle Radar Topography Mission”, Rev. Geophys., vol. 45, RG2004, doi: .CrossrefGoogle Scholar

  • [85]

    Tscheerning, C. C., (2010), “The use of Least-Squares Collocation for the processing of GOCE data”, Vermessung & Geoinformation, vol. 1, pp. 21–26.Google Scholar

  • [86]

    U.S. Geological Survey, (1996), “GTOPO30 Documentation”, http://lta.cr.usgs.gov/GTOPO30.Google Scholar

  • [87]

    U.S. Geological Survey, (2015), “Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global”, https://lta.cr.usgs.gov/SRTM1Arc.Google Scholar

  • [88]

    Vapnik, N. V., (1998), “Statistical Learning Theory”, John Wiley & Sons, New York.Google Scholar

  • [89]

    Varga, M. and Tomislav Bašić, (2015), “Accuracy validation and comparison of global digital elevation models over Croatia”, International Journal of Remote Sensing, vol. 36, no. 1, pp. 170–189, doi: .CrossrefGoogle Scholar

  • [90]

    Wang, J., Y. Hu, and Zhou, J., (2009), “Combining model for regional GPS height conversion based on least squares support vector machines”, Proceedings – 2009 International Conference on Environmental Science and Information Application Technology, ESIAT 2009 vol. 2, no. 2, pp. 639–641. doi: .CrossrefGoogle Scholar

  • [91]

    Wechsler, S. P., (2003), “Perceptions of Digital Elevation Model uncertainty by DEM users.” URISA Journal, vol. 15, no. 2, pp. 57–64.Google Scholar

  • [92]

    Yastikli, N., Koçak, G., and Büyüksalih, G., (2006), “Accuracy and morphological analysis of GTOPO30 and SRTM X-C band DEMS in the test area Istanbul”, ISPRS Topographic mapping from space with special emphasis on small satellite, Vol. XXXVI-1/W41, 2006, ISPRS Congress, 14–16 Feb. 2006, Ankara, Turkey, 6th session.

  • [93]

    Ye, J. and Xiong, T., (2007), “SVM versus least squares SVM”, Proc. 7th artificial intelligence and statistics, 21–24 March 2007, San Juan, Puerto Rico, Vol. 2, pp. 644–651.Google Scholar

  • [94]

    Zaletnyik, P., Völguesi, L., and Paláncz, B., (2008), “Modelling local GPS/levelling geoid undulations using Support Vector Machine”, Periodica Polytechnica, Civil Engineering, vol. 52, no. 1, pp. 39–43, web: http://www.pp.bme.hu/ci.CrossrefGoogle Scholar

  • [95]

    Zaletnyik, P., Völguesi, L., Kirchner, I., and Paláncz, B., (2007), “Combination of GPS/Leveling and gravimetric geoid by using the thin plate spline interpolation technique via finite element method”, Journal of Applied Geodesy, vol. 1, no. 2007, pp. 233–239 doi: .CrossrefGoogle Scholar

  • [96]

    Závoti, J. and Kalmár, J., (2016), “A comparison of different solutions of the Bursa–Wolf model and of the 3D, 7-parameter datum transformation”, Acta Geodaetica et Geophysica, vol. 51, no. 2, pp. 245–256.CrossrefGoogle Scholar

About the article

Received: 2018-12-25

Accepted: 2019-02-10

Published Online: 2019-03-05

Published in Print: 2019-07-26

Citation Information: Journal of Applied Geodesy, Volume 13, Issue 3, Pages 159–177, ISSN (Online) 1862-9024, ISSN (Print) 1862-9016, DOI: https://doi.org/10.1515/jag-2018-0050.

Export Citation

© 2019 Walter de Gruyter GmbH, Berlin/Boston.Get Permission

Comments (0)

Please log in or register to comment.
Log in