The application of an artificial neural network for 2D coordinate transformation

Author:

Abbas Ahmed Imad1,Alhamadani Oday Y. M.2,Mohammed Mamoun Ubaid1

Affiliation:

1. Surveying Engineering Department, Middle Technical University , Baghdad , Iraq

2. Surveying Engineering Department, University of Baghdad , Baghdad , Iraq

Abstract

Abstract Clark1880, WGS1984, and ITRF08 are the reference systems used in Iraq. The ITRF08 and WGS84 represent the global reference frames. In the majority of instances, the transformation from one coordinate system to another is required. The ability of the artificial neural network (ANN) to identify the connection between two coordinate systems without the need for a mathematical model is one of its most significant benefits. In this study, an ANN was employed for two-dimensional coordinate transformation from local Clark1880 to the global reference system ITRF08. To accomplish so, 68 stations with known coordinates in both systems were utilized in this research and were split into two groups: the first set of data (38 stations) was used as the training data and the second set of data (38 stations) was used as the validation data. A root-mean-square error (RMSE) was used to examine the performance of each transformation. The results showed that the RMSE using the ANN was 0.08 m in the east and 0.17 m in the north. The results indicated that the ANN can be used for 2D coordinate transformation with the results that are better than those of the authorized techniques such as 2D conformal transformation and 2D conformal least square.

Publisher

Walter de Gruyter GmbH

Subject

Artificial Intelligence,Information Systems,Software

Reference28 articles.

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3. Konakoglu B, Cakır L, Gökalp E. 2D coordinate transformation using artificial neural networks. Int Arch Photogram, Remote Sens Spat Inf Sci. 2016;42:183–6.

4. Nunes I, Dasilva HS. Artificial neural networks: a practical course. Brazil: Springer; 2018.

5. Bhasin H. Python basics: a self-teaching Introduction. LLC: Stylus Publishing; 2018.

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