@inproceedings{e2b8cac14ba2443b88c41bd16bd57c50,
title = "A multi-layer CRF based methodology for improving crowdsourced street semantics",
abstract = "This paper presents an intuitive and novel method for improving the semantic quality of streets in crowdsourced maps. Two factors negatively affecting the quality are incorrect and ambiguous semantics. Toward overcoming these, a multi-layer CRF based model is proposed that performs a simultaneous hierarchical classification of streets into fine-grained (crowdsourced; therefore, rich but ambiguous) and coarse-grained (familiar and standard) semantics. Inference is performed using Lazy Flipper algorithm which is fast for street network consisting of several hundred thousand streets. The model achieves a classification accuracy of 61\% for fine-grained classification and 77\% for coarse-grained classification respectively.",
keywords = "Conditional Random Fields, Hierarchical Classification, Open-StreetMap, Semantics, Street Networks",
author = "Musfira Jilani and Padraig Corcoran and Michela Bertolotto",
note = "Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 11th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2018 ; Conference date: 06-11-2018",
year = "2018",
month = nov,
day = "6",
doi = "10.1145/3283207.3283210",
language = "English",
series = "IWCTS 2018 - Proceedings of the 11th ACM SIGSPATIAL International Workshop on Computational Transportation Science",
publisher = "Association for Computing Machinery (ACM)",
pages = "29--38",
booktitle = "IWCTS 2018 - Proceedings of the 11th ACM SIGSPATIAL International Workshop on Computational Transportation Science",
address = "United States",
}