  In this contribution, we introduce a multilevel approximation method with
T-splines for fitting scattered point clouds iteratively, with an application
to land remote sensing. This new procedure provides a local surface
approximation by an explicit computation of the control points and is called a
multilevel T-splines approximation (MTA). It is computationally efficient
compared with the traditional global least-squares (LS) approach, which may
fail when there is an unfavourable point density from a given refinement level.
We validate our approach within a simulated framework and apply it to two real
datasets: (i) a surface with holes scanned with a terrestrial laser scanner,
and (ii) a patch on a sand-dune in the Netherlands. Both examples highlight the
potential of the MTA for rapidly fitting large and noisy point clouds with
variable point density and with similar results compared to the global LS
approximation.
