The Mean Shift (MS) algorithm allows to identify clusters that are catchment areas of modes of a probability density function (pdf). We propose to use a multiscale and hierarchical implementation of the algorithm to process grid data of population and identify automatically urban centers and their dependant sub-centers through scales. The multiscale structure is obtained by increasing iteratively the bandwidth of the kernel used to define the pdf on which the MS algorithm works. This will induce a hierarchical structure over clusters since modes will merge together when the bandwidth parameter increases.