Hight-Precision 3D Coordinate Transformation Using XGBoost Regression: A Machine Learning Approach for Geospatial Data

Hossamaldeen Mohamed; Abubakr Hassan; Maab Kamaleldeen

doi:10.31127/tuje.1702675

Hight-Precision 3D Coordinate Transformation Using XGBoost Regression: A Machine Learning Approach for Geospatial Data

Abstract

Traditional methods for 3D coordinate transformation often struggle with complex mathematical computations. This study presents a machine learning approach using Extreme Gradient Boosting (XGBoost) to achieve high-precision coordinate transformations between measurement systems. We developed three specialized XGBoost models (X, Y, Z axes) that learn the transformation rules directly from data, eliminating the need for predefined mathematical models. The framework processed raw coordinate measurements through careful data cleaning and splitting (80% training, 20% testing), intentionally avoiding normalization to preserve transformation relationships. Results demonstrated exceptional transformation accuracy, with R² scores of 0.9999 (X), 0.9996 (Y), and 0.9975 (Z), and RMSE values as low as 0.185 units. Error analysis showed maximum deviations under 1.5 units across all axes, while 3D visualization confirmed the model's ability to maintain geometric relationships during transformation. The independent axis modeling approach proved particularly effective for coordinate system conversions, capturing axis-specific transformation characteristics without cross-contamination. This work establishes XGBoost as a powerful alternative to conventional transformation methods, offering superior accuracy for applications in geodesy, photogrammetry, and CAD systems. Future enhancements could incorporate hybrid models that combine the strengths of parametric transformations with machine learning refinements.

Keywords

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Details

Primary Language

English

Subjects

Geomatic Engineering (Other)

Journal Section

Research Article

Authors

Hossamaldeen Mohamed ^*
0009-0008-4128-9329
Sudan

Abubakr Hassan
0000-0003-1998-4559
Sudan

Maab Kamaleldeen
0009-0009-1225-8029
Sudan

Early Pub Date

October 31, 2025

Publication Date

December 16, 2025

Submission Date

May 20, 2025

Acceptance Date

October 28, 2025

Published in Issue

Year 2026 Volume: 10 Number: 1

DOI

https://doi.org/10.31127/tuje.1702675

IZ

https://izlik.org/JA67NE68GD

Cite

RIS / Bibtex

APA

Mohamed, H., Hassan, A., & Kamaleldeen, M. (2025). Hight-Precision 3D Coordinate Transformation Using XGBoost Regression: A Machine Learning Approach for Geospatial Data. Turkish Journal of Engineering, 10(1), 91-102. https://doi.org/10.31127/tuje.1702675

AMA

1.Mohamed H, Hassan A, Kamaleldeen M. Hight-Precision 3D Coordinate Transformation Using XGBoost Regression: A Machine Learning Approach for Geospatial Data. TUJE. 2025;10(1):91-102. doi:10.31127/tuje.1702675

Chicago

Mohamed, Hossamaldeen, Abubakr Hassan, and Maab Kamaleldeen. 2025. “Hight-Precision 3D Coordinate Transformation Using XGBoost Regression: A Machine Learning Approach for Geospatial Data”. Turkish Journal of Engineering 10 (1): 91-102. https://doi.org/10.31127/tuje.1702675.

EndNote

Mohamed H, Hassan A, Kamaleldeen M (December 1, 2025) Hight-Precision 3D Coordinate Transformation Using XGBoost Regression: A Machine Learning Approach for Geospatial Data. Turkish Journal of Engineering 10 1 91–102.

IEEE

[1]H. Mohamed, A. Hassan, and M. Kamaleldeen, “Hight-Precision 3D Coordinate Transformation Using XGBoost Regression: A Machine Learning Approach for Geospatial Data”, TUJE, vol. 10, no. 1, pp. 91–102, Dec. 2025, doi: 10.31127/tuje.1702675.

ISNAD

Mohamed, Hossamaldeen - Hassan, Abubakr - Kamaleldeen, Maab. “Hight-Precision 3D Coordinate Transformation Using XGBoost Regression: A Machine Learning Approach for Geospatial Data”. Turkish Journal of Engineering 10/1 (December 1, 2025): 91-102. https://doi.org/10.31127/tuje.1702675.

JAMA

1.Mohamed H, Hassan A, Kamaleldeen M. Hight-Precision 3D Coordinate Transformation Using XGBoost Regression: A Machine Learning Approach for Geospatial Data. TUJE. 2025;10:91–102.

MLA

Mohamed, Hossamaldeen, et al. “Hight-Precision 3D Coordinate Transformation Using XGBoost Regression: A Machine Learning Approach for Geospatial Data”. Turkish Journal of Engineering, vol. 10, no. 1, Dec. 2025, pp. 91-102, doi:10.31127/tuje.1702675.

Vancouver

1.Hossamaldeen Mohamed, Abubakr Hassan, Maab Kamaleldeen. Hight-Precision 3D Coordinate Transformation Using XGBoost Regression: A Machine Learning Approach for Geospatial Data. TUJE. 2025 Dec. 1;10(1):91-102. doi:10.31127/tuje.1702675