@inproceedings{b2dffc0946874c9bb35395f5e707f2f7,
title = "Inferring semantics from geometry - The case of street networks",
abstract = "This paper proposes a method for automatically inferring semantic type information for a street network from its corresponding geometrical representation. Specifically, a street network is modelled as a probabilistic graphical model and semantic type information is inferred by performing learning and inference with respect to this model. Learning is performed using a maximum-margin approach while inference is performed using a fusion moves approach. The proposed model captures features relating to individual streets, such as linearity, as well as features relating to the relationships between streets such as the co-occurrence of semantic types. On a large street network containing 32,412 street segments, the proposed model achieves precision and recall values of 68\% and 65\% respectively. One application of this work is the automation of street network mapping.",
keywords = "Geometry, Machine learning, Semantics",
author = "Padraig Corcoran and Musfira Jilani and Peter Mooney and Michela Bertolotto",
note = "Publisher Copyright: {\textcopyright} 2015 ACM.; 23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015 ; Conference date: 03-11-2015 Through 06-11-2015",
year = "2015",
month = nov,
day = "3",
doi = "10.1145/2820783.2820822",
language = "English",
series = "GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems",
publisher = "Association for Computing Machinery (ACM)",
editor = "Yan Huang and Mohamed Ali and Jagan Sankaranarayanan and Matthias Renz and Michael Gertz",
booktitle = "23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2015",
address = "United States",
}