Automatic Sampling of Geographic objects
Artificial Intelligence
2012-04-23 v1
Abstract
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.
Cite
@article{arxiv.1204.4541,
title = {Automatic Sampling of Geographic objects},
author = {Patrick Taillandier and Julien Gaffuri},
journal= {arXiv preprint arXiv:1204.4541},
year = {2012}
}