@ARTICLE{Ziggah_Yao_Yevenyo_2D_2018,
 author={Ziggah, Yao Yevenyo and Issaka, Yakubu and Laari, Prosper Basommi and Hui, Zhenyang},
 volume={vol. 67},
 number={No 2},
 pages={321-343},
 journal={Geodesy and Cartography},
 howpublished={online},
 year={2018},
 publisher={Commitee on Geodesy PAS},
 abstract={Land surveyors, photogrammetrists, remote sensing engineers and professionals in the Earth sciences are often faced with the task of transferring coordinates from one geodetic datum into another to serve their desired purpose. The essence is to create compatibility between data related to different geodetic reference frames for geospatial applications. Strictly speaking, conventional techniques of conformal, affine and projective transformation models are mostly used to accomplish such task. With developing countries like Ghana where there is no immediate plans to establish geocentric datum and still rely on the astro-geodetic datums as it national mapping reference surface, there is the urgent need to explore the suitability of other transformation methods. In this study, an effort has been made to explore the proficiency of the Extreme Learning Machine (ELM) as a novel alternative coordinate transformation method. The proposed ELM approach was applied to data found in the Ghana geodetic reference network. The ELM transformation result has been analysed and compared with benchmark methods of backpropagation neural network (BPNN), radial basis function neural network (RBFNN), two-dimensional (2D) affine and 2D conformal. The overall study results indicate that the ELM can produce comparable transformation results to the widely used BPNN and RBFNN, but better than the 2D affine and 2D conformal. The results produced by ELM has demonstrated it as a promising tool for coordinate transformation in Ghana.},
 type={Artykuły / Articles},
 title={2D Cadastral Coordinate Transformation using extreme learning machine technique},
 URL={http://journals.pan.pl/Content/103274/PDF/art_10%20geodesy-2.pdf},
 keywords={coordinate transformation, extreme learning machine, backpropagation neural network, radial basis function neural network, geodetic datum},
}