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.
Cite
Text
Jilani et al. "Machine Learning for Crowdsourced Spatial Data." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016. doi:10.1007/978-3-319-46131-1_38Markdown
[Jilani et al. "Machine Learning for Crowdsourced Spatial Data." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016.](https://mlanthology.org/ecmlpkdd/2016/jilani2016ecmlpkdd-machine/) doi:10.1007/978-3-319-46131-1_38BibTeX
@inproceedings{jilani2016ecmlpkdd-machine,
title = {{Machine Learning for Crowdsourced Spatial Data}},
author = {Jilani, Musfira and Corcoran, Padraig and Bertolotto, Michela},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2016},
pages = {294-297},
doi = {10.1007/978-3-319-46131-1_38},
url = {https://mlanthology.org/ecmlpkdd/2016/jilani2016ecmlpkdd-machine/}
}