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Road Network Fusion for Incremental Map Updates

  • Rade Stanojevic
  • Sofiane Abbar
  • Saravanan Thirumuruganathan
  • Gianmarco De Francisci Morales
  • Sanjay Chawla
  • Fethi Filali
  • Ahid Aleimat
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

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.

Keywords

Map fusion Map inference Road closures 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Rade Stanojevic
    • 1
  • Sofiane Abbar
    • 1
  • Saravanan Thirumuruganathan
    • 1
  • Gianmarco De Francisci Morales
    • 1
  • Sanjay Chawla
    • 1
  • Fethi Filali
    • 2
  • Ahid Aleimat
    • 2
  1. 1.Qatar Computing Research InstituteHBKUDohaQatar
  2. 2.Qatar Mobility Innovation CenterQSTPDohaQatar

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