Using machine learning and data enrichment in the selection of roads for small-scale maps

ABSTRACT

Making decisions about which objects to keep or omit is challenging in map design. This process, called selection, constitutes the first operation in cartographic generalization. In this research, a method of automatic road selection for creating small-scale maps using machine learning and data enrichment is proposed. First, the problem of contextual information scarcity concerning roads in the source database is addressed. Additional information concerning the relations between roads and other objects was added (such as centrality and proximity measures). Second, machine learning is used to design automatic selection models based on enriched information. Third, three different road selection approaches are implemented. The baseline approach is following the official map design guidelines. The second approach is based on machine learning using the enriched road database. The third approach is based on an existing structural model. The results of all approaches are compared to existing atlas maps designed by experienced cartographers. The results of the Machine Learning Approaches were most similar to the atlas maps (between 81% and 90% accuracy). The least efficient approaches were the Structural Approach with 32% and the Guidelines Approach with 44% accuracy. We conclude that enriching road data with new contextual information concerning roads and using machine learning is beneficial as the achieved results outperform both Guidelines and Structural Approaches.

KEYWORDS:

• Road network
• cartographic generalization
• selection
• small-scale maps
• machine learning
• data enrichment

Acknowledgments

This research was funded by the National Science Centre, Poland, grant number UMO-2020/37/B/HS4/02605, “Improving Settlement and Road Network Design for Maps of Small Scales Using Artificial Intelligence and Graph Theory.” Robert Weibel also acknowledges support by the Swiss National Science Foundation through project number 200021_204081, DeepGeneralization.

Disclosure statement

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

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This work was supported by the National Science Centre Poland [UMO-2020/37/B/HS4/02605].