  The accuracy of digital elevation models (DEMs) in urban areas is influenced
by numerous factors including land cover and terrain irregularities. Moreover,
building artifacts in global DEMs cause artificial blocking of surface flow
pathways. This compromises their quality and adequacy for hydrological and
environmental modelling in urban landscapes where precise and accurate terrain
information is needed. In this study, the extreme gradient boosting (XGBoost)
ensemble algorithm is adopted for enhancing the accuracy of two
medium-resolution 30m DEMs over Cape Town, South Africa: Copernicus GLO-30 and
ALOS World 3D (AW3D). XGBoost is a scalable, portable and versatile gradient
boosting library that can solve many environmental modelling problems. The
training datasets are comprised of eleven predictor variables including
elevation, urban footprints, slope, aspect, surface roughness, topographic
position index, terrain ruggedness index, terrain surface texture, vector
roughness measure, forest cover and bare ground cover. The target variable
(elevation error) was calculated with respect to highly accurate airborne
LiDAR. After training and testing, the model was applied for correcting the
DEMs at two implementation sites. The correction achieved significant accuracy
gains which are competitive with other proposed methods. The root mean square
error (RMSE) of Copernicus DEM improved by 46 to 53% while the RMSE of AW3D DEM
improved by 72 to 73%. These results showcase the potential of gradient boosted
trees for enhancing the quality of DEMs, and for improved hydrological
modelling in urban catchments.
