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.
CITATION STYLE
Jilani, M., Corcoran, P., & Bertolotto, M. (2016). Machine learning for crowdsourced spatial data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9853 LNCS, pp. 294–297). Springer Verlag. https://doi.org/10.1007/978-3-319-46131-1_38
Mendeley helps you to discover research relevant for your work.