2D Cadastral Coordinate Transformation using extreme learning machine technique

The Geodesy and Cartography is a semi-annually scientific journal publishing peer-reviewed articles with original solutions of theoretical, experimental or applicable problems in the field of geodesy, surveying engineering, cartography and GIS, cadastre and land management, photogrammetry, remote sensing and related disciplines. The Geodesy and Cartography is published under the umbrella of the Committee on Geodesy of the Polish Academy of Sciences (PAS). Printed version ISSN 2080-6736, On-line version ISSN 2300-2581 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.