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Traditional vs. Machine-Learning Techniques for OSM Quality Assessment

Traditional vs. Machine-Learning Techniques for OSM Quality Assessment

Musfira Jilani, Michela Bertolotto, Padraig Corcoran, Amerah Alghanim
Copyright: © 2017 |Pages: 18
ISBN13: 9781522524465|ISBN10: 1522524460|EISBN13: 9781522524472
DOI: 10.4018/978-1-5225-2446-5.ch003
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MLA

Jilani, Musfira, et al. "Traditional vs. Machine-Learning Techniques for OSM Quality Assessment." Volunteered Geographic Information and the Future of Geospatial Data, edited by Cláudio Elízio Calazans Campelo, et al., IGI Global, 2017, pp. 47-64. https://doi.org/10.4018/978-1-5225-2446-5.ch003

APA

Jilani, M., Bertolotto, M., Corcoran, P., & Alghanim, A. (2017). Traditional vs. Machine-Learning Techniques for OSM Quality Assessment. In C. Calazans Campelo, M. Bertolotto, & P. Corcoran (Eds.), Volunteered Geographic Information and the Future of Geospatial Data (pp. 47-64). IGI Global. https://doi.org/10.4018/978-1-5225-2446-5.ch003

Chicago

Jilani, Musfira, et al. "Traditional vs. Machine-Learning Techniques for OSM Quality Assessment." In Volunteered Geographic Information and the Future of Geospatial Data, edited by Cláudio Elízio Calazans Campelo, Michela Bertolotto, and Padraig Corcoran, 47-64. Hershey, PA: IGI Global, 2017. https://doi.org/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is an expensive process and the vastness and dynamics of our world usually render centralized and authoritative maps outdated and incomplete.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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