Prior any satellite technology developments, the geodetic networks of a country were realized from a topocentric datum, and hence the respective cartography was performed. With availability of Global Navigation Satellite Systems-GNSS, cartography needs to be updated and referenced to a geocentric datum to be compatible with this technology. Cartography in Ecuador has been performed using the PSAD56 (Provisional South American Datum 1956) systems, nevertheless it’s necessary to have inside the system SIRGAS (SIstema de Referencia Geocéntrico para las AmericaS). This transformation between PSAD56 to SIRGAS use seven transformation parameters calculated with the method Helmert. These parameters, in case of Ecuador are compatible for scales of 1:25 000 or less, that does not satisfy the requirements on applications for major scales. In this study, the technique of neural networks is demonstrated as an alternative for improving the processing of UTM planes coordinates E, N (East, North) from PSAD56 to SIRGAS. Therefore, from the coordinates E, N, of the two systems, four transformation parameters were calculated (two of translation, one of rotation, and one scale difference) using the technique bidimensional transformation. Additionally, the same coordinates were used to training Multilayer Artificial Neural Network -MANN, in which the inputs are the coordinates E, N in PSAD56 and output are the coordinates E, N in SIRGAS. Both the two-dimensional transformation and ANN were used as control points to determine the differences between the mentioned methods. The results imply that, the coordinates transformation obtained with the artificial neural network multilayer trained have been improving the results that the bidimensional transformation, and compatible to scales 1:5000.
Haykin, S. (2001). Neural Network: A Comprehensive Foundation. Second Edition. Hamilton, Ontario, Canada.
Krasnopolsky, V. (2013). The Application of Neural Networks in the Earth System Sciences: Neural Networks Emulations for Complex Multidimensional Mappings. Springer, New York Seeber, G. (1993). Satellite Geodesy: Foundations Methods and Applications. W de Guyter. Berlin-New York Tierra, A. Dalazoana, R. & De Freitas, S. (2008). Using Artifi cial Neural Network To Improve The Transformation of Coordinates Between Classical Geodetic Reference Frames. Computers & Geosciences, p. 181-189, DOI:10.1016, Netherlands.
Tierra, A., De Freitas, S. & Guevara, P. (2009). Using Artifi cial Neural Network to Transformation of Coordinates from PSAD56 to SIRGAS95. International Association of Geodesy Symposia: Geodetic Reference Frames. Springer-Verlag, Berlin-Germany, Vol 134, p. 173-178, DOI: 10.1007/978-3-642-00860-3
He, X & Xu, Sh. (2009). Process Neural Networks: Theory Applications. Springer-Verlag, Berlin Heidelberg.
Yilmaz, E. & Akhmet, M. (2014). Neural Networks with Discontinuous: Impact Activations. Springer, Neww York.
You R & Hwang, H. (2006). Coordinate Transformation between Two Geodetic Datums of Taiwan by Least-Squares Collocation. J. Surv. Eng., 132(2), 64-70.