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Machine learning for crowdsourced spatial data

Jilani, M., Corcoran, Padraig and Bertolotto, M. 2016. Machine learning for crowdsourced spatial data. Presented at: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery, Riva del Garda, Italy, 19-23 September 2016. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery. Lecture Notes in Artificial Intellingence Springer, pp. 294-297.

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Abstract

Recent years have seen a significant increase in the number of applications requiring accurate and up-to-date spatial data. In this context crowdsourced maps such as OpenStreetMap (OSM) have the potential to provide a free and timely representation of our world. However, one factor that negatively influences the proliferation of these maps is the uncertainty about their data quality. This paper presents structured and unstructured machine learning methods to automatically assess and improve the semantic quality of streets in the OSM database.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: Springer
ISBN: 9783319461304
Last Modified: 04 Jun 2017 09:23
URI: http://orca.cf.ac.uk/id/eprint/94322

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