Inferring Semantics from Geometry - the Case of Street Networks
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|Title:||Inferring Semantics from Geometry - the Case of Street Networks||Authors:||Corcoran, Padraig
|Permanent link:||http://hdl.handle.net/10197/7375||Date:||6-Nov-2015||Online since:||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.||Funding Details:||Irish Research Council||Type of material:||Conference Publication||Publisher:||ACM||Keywords:||Geometry; Semantics; Machine learning||Other versions:||http://sigspatial2015.sigspatial.org/||Language:||en||Status of Item:||Peer reviewed||Is 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|
|Appears in Collections:||Computer Science Research Collection|
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