Utilizing Artificial Neural Networks for 2D Coordinate Transformation: A Case Study on the Reference System of Iraq

Abstract

The study seeks to assess the efficacy of utilizing a Back Propagation Artificial Neural Network (BPANN) as an alternative to conventional methodologies for 2D coordinate transformation. Converting the previously used reference system in Iraq to the global system is one of the main requirements that play a main role in various applications requiring the standardization of geographic referencing for different data, including geospatial applications, remote sensing, image processing, and more. This necessitates researching and finding the most suitable methods to ensure high accuracy in the results. The Karbala 1979 (Clark 1880) of the Iraqi national geodetic network and the Iraqi Geospatial Reference System (IGRS) were currently used as geodetic reference frames in Iraq. In the context of transitioning between the local system (Karbala 1979) and the global system, specifically IGRS (ITRF2000 at epoch 1997.0), a crucial procedure involves the conversion of two-dimensional (2D) coordinate data. This conversion employed the BPANN method and conventional techniques such as affine and polynomial. The outcomes derived from the BPANN methodology are juxtaposed against those from the affine and polynomial methodologies. The findings reveal that the effective implementation of each approach within this study is contingent upon the spatial arrangement of the control points leveraged to develop the transformation model. Additionally, the root mean square error (RMSE) of BPANN ranging from 3 to 5 cm closely aligns with the outcomes achieved through affine and polynomial methods.