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Using Crowdsourced Trajectories for Automated OSM Data Entry Approach.

Anahid Basiri1, Pouria Amirian2, Peter Mooney3

  • 1The Nottingham Geospatial Institute, The University of Nottingham, Nottingham NG7 2TU, UK. anahid.basiri@nottingham.ac.uk.

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|September 21, 2016
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Summary
This summary is machine-generated.

This study introduces an automated method using movement trajectories to improve the accuracy of crowdsourced geospatial data in OpenStreetMap (OSM). The approach enhances data quality by analyzing contributor paths to refine map features.

Keywords:
OpenStreetMapcompletenesscrowdsourcingspatial data qualitytrajectory data mining

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Area of Science:

  • Geographic Information Science
  • Data Mining
  • Crowdsourcing

Background:

  • OpenStreetMap (OSM) relies on crowdsourced data, but contributor inputs often suffer from inaccuracies, incompleteness, and vagueness.
  • Issues stem from varying contributor experience, unfamiliarity with areas, inconsistent attribute interpretation, and differing mapping enthusiasm.
  • Existing challenges impact the overall quality and reliability of OSM's geospatial database.

Purpose of the Study:

  • To propose an automated mechanism for improving the quality of crowdsourced geospatial data in OpenStreetMap.
  • To leverage raw spatial data, specifically contributor movement trajectories, to minimize data uncertainty and errors.
  • To enhance the accuracy and consistency of geometric and attribute information within the OSM database.

Main Methods:

  • Utilizes data mining techniques to analyze raw trajectory datasets contributed by OSM users.
  • Extracts patterns and rules linking user movement characteristics to geographic feature geometry and attributes.
  • Develops an automatic mechanism for inserting or editing features in the OSM database based on derived rules.

Main Results:

  • Successfully generated new features with demonstrably higher spatial quality.
  • Automatically inserted improved features into the OpenStreetMap database.
  • The method effectively addresses inaccuracies and inconsistencies in crowdsourced geospatial data.

Conclusions:

  • User movement trajectories contain valuable information for improving crowdsourced geospatial data quality.
  • An automated approach analyzing these trajectories can significantly enhance the accuracy of OSM data.
  • This method offers a scalable solution for refining the global geospatial database.