Name
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46131-1_38 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
Probabilistic graphical modelling,Crowdsourced spatial data,Street networks,Semantics | Spatial analysis,Data quality,Information retrieval,Computer science,Artificial intelligence,Semantics,Machine learning | Conference |
Volume | ISSN | Citations |
9853 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Musfira Jilani | 1 | 13 | 2.70 |
| Padraig Corcoran | 2 | 191 | 23.08 |
| Michela Bertolotto | 3 | 863 | 91.77 |