A Road Network Selection Process Based on Data Enrichment and Structure Detection

In the context of geographical database generalization, this article deals with a generic process for road network selection. It is based on the geographical context, which is made explicit, and on the preservation of characteristic structure. It relies on literature that is adapted and collected. The first step is to detect significant structures and patterns of the road network such as roundabouts or highway interchanges. It allows the initial dataset to be enriched with explicit geographic structures that were implicit in the initial data. It helps both to make the geographical context explicit and to preserve characteristic structures. Then this enrichment is used as knowledge input for the following step: that is, the selection of roads in rural areas using graph theory techniques. After that, urban roads are selected by means of a block aggregation complex algorithm. Continuity between urban and rural areas is guaranteed by modelling continuity using strokes. Finally, the previously detected characteristic structures are typified to maintain their properties in the selected network. This automated process has been fully implemented on Clarity T and tested on large datasets.