Road network fusion for incremental map updates

Research output: Contribution to journalConference article

4 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)91-109
Number of pages19
JournalLecture Notes in Geoinformation and Cartography
Issue number208669
Publication statusPublished - 1 Jan 2018


  • Map fusion
  • Map inference
  • Road closures

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Geography, Planning and Development
  • Earth-Surface Processes
  • Computers in Earth Sciences

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