Comparison of machine learning and statistical approaches for digital elevation model (DEM) correction: interim results
<|reference_start|>Comparison of machine learning and statistical approaches for digital elevation model (DEM) correction: interim results: Several methods have been proposed for correcting the elevation bias in digital elevation models (DEMs) for example, linear regression. Nowadays, supervised machine learning enables the modelling of complex relationships between variables, and has been deployed by researchers in a variety of fields. In the existing literature, several studies have adopted either machine learning or statistical approaches in the task of DEM correction. However, to our knowledge, none of these studies have compared the performance of both approaches, especially with regard to open-access global DEMs. Our previous work has already shown the potential of machine learning approaches, specifically gradient boosted decision trees (GBDTs) for DEM correction. In this study, we share some results from the comparison of three recent implementations of gradient boosted decision trees (XGBoost, LightGBM and CatBoost), versus multiple linear regression (MLR) for enhancing the vertical accuracy of 30 m Copernicus and AW3D global DEMs in Cape Town, South Africa.<|reference_end|>
@article{okolie2024comparison,
    title={Comparison of machine learning and statistical approaches for digital
  elevation model (DEM) correction: interim results},
    author={Chukwuma Okolie, Adedayo Adeleke, Julian Smit, Jon Mills, Iyke
  Maduako, Caleb Ogbeta},
    journal={arXiv preprint arXiv:2402.06688},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06688},
    primaryClass={cs.LG}
}