Automatic Sampling of Geographic objects
This addresses sampling needs for geographic data processes, but it appears incremental as it applies existing clustering techniques to a specific domain.
The paper tackles the problem of selecting small representative samples from large geographic datasets for expert appraisal or computationally intensive processes, proposing a clustering-based method that divides objects into clusters and selects the most representative ones from each, with a case-study showing it selects relevant samples.
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.