Recognition of group patterns in geological maps by building similarity networks

Abstract

The recognition of structures is fundamental to map generalization, furnishing structural information that assists in choosing and parameterizing generalization operators. We specifically focus on the process of recognizing groups of small polygons in geological maps as a prerequisite to subsequent aggregation or typification operators. Proximity between polygons represents an essential criterion in identifying neighboring map objects. Here, network-based analysis is used for effective definition and refinement of candidate group members, applying criteria such as the distance between polygons, polygon size, shape, orientation, and feature attributes such as rock type. Starting off from the Delaunay triangulation of the polygon centroids, the global and local long edges, which initially define the network, are removed. The modified network is loaded with additional criteria, and edges are kept or removed based on the local similarities of the polygons they connect. This approach leads to more homogeneous, meaningful groups of polygon features in geological maps.

Keywords:

• Cartographic pattern detection
• map generalization
• geological mapping
• graph-based clustering
• similarity network

Acknowledgments

We acknowledge the support provided to the first author by the Swiss Federal Commission for Scholarships (FCS). The authors like to acknowledge their use of the Euriowie (including part of Campbells Creek) 1:25,000 Geological Map provided by the Department of Primary Industries - Mineral Resources, Geological Survey of New South Wales, NSW, Australia, which formed the basis for the experiments reported in this paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by a Ph.D. scholarship of the Swiss Federal Commission for Scholarships (FCS) under the Grant 2015.0750.