  Locally refined spline surfaces (LRB) is a representation well suited for
scattered data approximation. When a data set has local details in some areas
and is largely smooth in other, LR B-splines allow the spatial distribution of
degrees of freedom to follow the variations of the data set. An LRB surface
approximating a data set is refined in areas where the accuracy does not meet a
required tolerance. In this paper we address, in a systematic study, different
LRB refinement strategies and polynomial degrees for surface approximation. We
study their influence on data volume and accuracy when applied to geospatial
data sets with different structural behaviour. The performance of the
refinement strategies is to some degree coherent and the article concludes with
some recommendations. An overall evaluation indicates that bi-quadratic LRB are
preferable for the uses cases tested, and that the strategies we denote as
'full span' have the overall best performance.
