@inproceedings{74d9dc64c4ff46e1810eeb6db2c9565a,
title = "Machine learning for crowdsourced spatial data",
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.",
keywords = "Crowdsourced spatial data, Probabilistic graphical modelling, Semantics, Street networks",
author = "Musfira Jilani and Padraig Corcoran and Michela Bertolotto",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016 ; Conference date: 19-09-2016 Through 23-09-2016",
year = "2016",
doi = "10.1007/978-3-319-46131-1\_38",
language = "English",
isbn = "9783319461304",
series = "Lecture Notes in Computer Science",
publisher = "Springer Verlag",
pages = "294--297",
editor = "Bettina Berendt and Bj{\"o}rn Bringmann and Elisa Fromont and Gemma Garriga and Pauli Miettinen and Nikolaj Tatti and Volker Tresp",
booktitle = "Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings",
address = "Germany",
}