Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Traditional vs. Machine-Learning Techniques for OSM Quality Assessment

Traditional vs. Machine-Learning Techniques for OSM Quality Assessment
View Sample PDF
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: 2019
Pages: 19
Source title: Geospatial Intelligence: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-8054-6.ch022


View Traditional vs. Machine-Learning Techniques for OSM Quality Assessment on the publisher's website for pricing and purchasing information.


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is an expensive process and the vastness and dynamics of our world usually render centralized and authoritative maps outdated and incomplete.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

Related Content

Uchendu Eugene Chigbu. © 2019. 14 pages.
Mohamed Timoulali. © 2019. 13 pages.
Moulay Abdeslam Adad, El Hassane Semlali, Fatiha Ibannain. © 2019. 20 pages.
Moha El-Ayachi. © 2019. 16 pages.
Elmostaphi Elomari, Hassan Rhinane. © 2019. 18 pages.
Loubna El Mansouri, Said Lahssini, Rachid Hadria, Nadia Eddaif, Tarik Benabdelouahab, Asmae Dakir. © 2019. 24 pages.
Rachid Hadria, Loubna El Mansouri, Tarik Benabdelouhab, Pietro Ceccato. © 2019. 17 pages.
Body Bottom