Automatic Sampling of Geographic objects
  Today, one's disposes of large datasets composed of thousands of geographic
objects. However, for many processes, which require the appraisal of an expert
or much computational time, only a small part of these objects can be taken
into account. In this context, robust sampling methods become necessary. In
this paper, we propose a sampling method based on clustering techniques. Our
method consists in dividing the objects in clusters, then in selecting in each
cluster, the most representative objects. A case-study in the context of a
process dedicated to knowledge revision for geographic data generalisation is
presented. This case-study shows that our method allows to select relevant
samples of objects.
