Automatic Sampling of Geographic objects

TL;DR

This paper introduces a clustering-based sampling method for large geographic datasets, enabling the selection of representative objects to facilitate expert appraisal and reduce computational load.

Contribution

The paper presents a novel clustering approach for sampling geographic objects, improving the relevance of selected samples for data generalization tasks.

Findings

Effective selection of relevant geographic objects

Reduces computational time for data processing

Applicable to geographic data generalization

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.