Comparison of machine learning and statistical approaches for digital
  elevation model (DEM) correction: interim results
Chukwuma Okolie Adedayo Adeleke Julian Smit Jon Mills Iyke Maduako Caleb Ogbeta
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.