Automatic Sampling of Geographic objects

Patrick Taillandier (1) , Julien Gaffuri (2)

1 UMMISCO - Unité de modélisation mathématique et informatique des systèmes complexes [Bondy]
2 COGIT - Conception Objet et Généralisation de l'Information Topographique

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.

Mots clés: on-demand mapping.; machine learning; on-demand mapping; generalisation evaluation; man-machine dialogue

Domaines: Intelligence artificielle [cs.AI]

Patrick Taillandier, Julien Gaffuri. Automatic Sampling of Geographic objects. GIScience, 2010, Zurich, Switzerland. 5 p. ⟨hal-00689137⟩