Machine learning for crowdsourced spatial data

Jilani, M., Corcoran, Padraig ORCID: https://orcid.org/0000-0001-9731-3385 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. Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2016. Lecture Notes in Computer Science , vol.9853 Cham: 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:	978-3-319-46131-1
Last Modified:	01 Nov 2022 11:15
URI:	https://orca.cardiff.ac.uk/id/eprint/94322

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