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Inferring Semantics from Geometry - the Case of Street Networks
Date Issued
2015-11-06
Date Available
2016-01-15T09:48:10Z
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.
Sponsorship
Irish Research Council
Other Sponsorship
European Marie Curie International Outgoing Fellowship
Type of Material
Conference Publication
Publisher
ACM
Web versions
Language
English
Status of Item
Peer reviewed
Part of
Proceedings of the 23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Conference Details
23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2015), Seattle, Washington, USA, 3 - 6 November
This item is made available under a Creative Commons License
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