Welcome to the InfoSci Platform
Reference Hub3
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: © 2019 |Pages: 19
ISBN13: 9781522580546|ISBN10: 1522580549|EISBN13: 9781522580553
DOI: 10.4018/978-1-5225-8054-6.ch022
Cite Chapter Cite Chapter

MLA

Jilani, Musfira, et al. "Traditional vs. Machine-Learning Techniques for OSM Quality Assessment." Geospatial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2019, pp. 469-487. https://doi.org/10.4018/978-1-5225-8054-6.ch022

APA

Jilani, M., Bertolotto, M., Corcoran, P., & Alghanim, A. (2019). Traditional vs. Machine-Learning Techniques for OSM Quality Assessment. In I. Management Association (Ed.), Geospatial Intelligence: Concepts, Methodologies, Tools, and Applications (pp. 469-487). IGI Global. https://doi.org/10.4018/978-1-5225-8054-6.ch022

Chicago

Jilani, Musfira, et al. "Traditional vs. Machine-Learning Techniques for OSM Quality Assessment." In Geospatial Intelligence: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 469-487. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-8054-6.ch022

Export Reference

Mendeley
Favorite

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is an expensive process and the vastness and dynamics of our world usually render centralized and authoritative maps outdated and incomplete.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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.