  A simple greedy refinement procedure for the generation of data-adapted
triangulations is proposed and studied. Given a function of two variables, the
algorithm produces a hierarchy of triangulations and piecewise polynomial
approximations on these triangulations. The refinement procedure consists in
bisecting a triangle T in a direction which is chosen so as to minimize the
local approximation error in some prescribed norm between the approximated
function and its piecewise polynomial approximation after T is bisected.
  The hierarchical structure allows us to derive various approximation tools
such as multiresolution analysis, wavelet bases, adaptive triangulations based
either on greedy or optimal CART trees, as well as a simple encoding of the
corresponding triangulations. We give a general proof of convergence in the Lp
norm of all these approximations.
  Numerical tests performed in the case of piecewise linear approximation of
functions with analytic expressions or of numerical images illustrate the fact
that the refinement procedure generates triangles with an optimal aspect ratio
(which is dictated by the local Hessian of of the approximated function in case
of C2 functions).
