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Traditional vs. Machine-Learning Techniques for OSM Quality Assessment
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Author(s): Musfira Jilani (National University of Ireland, Ireland), Michela Bertolotto (University College Dublin, Ireland), Padraig Corcoran (Cardiff University, UK)and Amerah Alghanim (University College Dublin, Ireland)
Copyright: 2017
Pages: 18
Source title:
Volunteered Geographic Information and the Future of Geospatial Data
Source Author(s)/Editor(s): Cláudio Elízio Calazans Campelo (Federal University of Campina Grande, Brazil), Michela Bertolotto (University College Dublin, Ireland)and Padraig Corcoran (Cardiff University, UK)
DOI: 10.4018/978-1-5225-2446-5.ch003
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Abstract
Nowadays an ever-increasing number of applications require complete and up-to-date spatial data, in particular maps. However, mapping is an expensive process and the vastness and dynamics of our world usually render centralized and authoritative maps outdated and incomplete. In this context crowd-sourced maps have the potential to provide a complete, up-to-date, and free representation of our world. However, the proliferation of such maps largely remains limited due to concerns about their data quality. While most of the current data quality assessment mechanisms for such maps require referencing to authoritative maps, we argue that such referencing of a crowd-sourced spatial database is ineffective. Instead we focus on the use of machine learning techniques that we believe have the potential to not only allow the assessment but also to recommend the improvement of the quality of crowd-sourced maps without referencing to external databases. This chapter gives an overview of these approaches.
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