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Machine Learning for Crowdsourced Spatial Data

  • Musfira JilaniEmail author
  • Padraig Corcoran
  • Michela Bertolotto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9853)

Abstract

Recent years have seen a significant increase in the number of applications requiring accurate and up-to-date spatial data. In this context crowdsourced maps such as OpenStreetMap (OSM) have the potential to provide a free and timely representation of our world. However, one factor that negatively influences the proliferation of these maps is the uncertainty about their data quality. This paper presents structured and unstructured machine learning methods to automatically assess and improve the semantic quality of streets in the OSM database.

Keywords

Probabilistic graphical modelling Crowdsourced spatial data Street networks Semantics 

Notes

Acknowledgments

This work is supported by the Irish Research Council through the Embark Postgraduate Scholarship Scheme 2012.

References

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Musfira Jilani
    • 1
    Email author
  • Padraig Corcoran
    • 2
  • Michela Bertolotto
    • 1
  1. 1.School of Computer ScienceUniversity College DublinDublinIreland
  2. 2.School of Computer ScienceCardiff UniversityCardiffUK

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