Abstract
In the recent years a number of novel, automatic map-inference techniques have been proposed, which derive road-network from a cohort of GPS traces collected by a fleet of vehicles. In spite of considerable attention, these maps are imperfect in many ways: they create an abundance of spurious connections, have poor coverage, and are visually confusing. Hence, commercial and crowd-sourced mapping services heavily use human annotation to minimize the mapping errors. Consequently, their response to changes in the road network is inevitably slow. In this paper we describe MapFuse, a system which fuses a human-annotated map (e.g., OpenStreetMap) with any automatically inferred map, thus effectively enabling quick map updates. In addition to new road creation, we study in depth road closure, which have not been examined in the past. By leveraging solid, human-annotated maps with minor corrections, we derive maps which minimize the trajectory matching errors due to both road network change and imperfect map inference of fully-automatic approaches.
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Notes
- 1.
Biagioni F1-score is a well known metric for measuring the topological accuracy of a map and lies in the range [0, 1] with 0 being absolutely wrong map, and 1 being a perfect map.
- 2.
Influenced by a rapid construction of the city metro and a number of ongoing infrastructure projects.
- 3.
We believe using another node-centrality measure would likely give similar results, though we do not evaluate the impact of the choice of centrality measure in this work. However, the use of betweenness is consistent with the problem definition in Sect. 3.
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Stanojevic, R. et al. (2018). Road Network Fusion for Incremental Map Updates. In: Kiefer, P., Huang, H., Van de Weghe, N., Raubal, M. (eds) Progress in Location Based Services 2018. LBS 2018. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-71470-7_5
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