Abstract
Abstract. Automatic map generalization is a complex task that is still a research problem and requires the development of research prototypes before being usable in productive map processes. In the meantime, reproducible research principles are becoming a standard. Publishing reproducible research means that researchers share their code and their data so that other researchers might be able to reproduce the published experiments, in order to check them, extend them, or compare them to their own experiments. Open source software is a key tool to share code and software, and CartAGen is the first open source research platform that tackles the overall map generalization problem: not only the building blocks that are generalization algorithms, but also methods to chain them, and spatial analysis tools necessary for data enrichment. This paper presents the CartAGen platform, its architecture and its components. The main component of the platform is the implementation of several multi-agent based models of the literature such as AGENT, CartACom, GAEL, CollaGen, or DIOGEN. The paper also explains and discusses different ways, as a researcher, to use or to contribute to CartAGen.
Highlights
There is not a unique way to generalize a map and the past attempts to automate this complex process led to very different prototypes, which most of the time, can be neither compared nor combined
We only found the work of Bergenheim et al (2009) that proposes generalization algorithms in the Grass open source platform
5.3 Using CartAGen in National Mapping Agencies Research in map generalization has long been driven by the needs of National Mapping Agencies (NMA) and for a few years, several automatic processes have been used in production, using commercial software solutions (Duchêne et al, 2014)
Summary
There is not a unique way to generalize a map and the past attempts to automate this complex process led to very different prototypes, which most of the time, can be neither compared nor combined. This limitation does not really allow map generalization research to build upon past research: even if we know past proposals of models and algorithms, we cannot reuse the prototypes and their code.
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