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Inferring semantics from geometry - the case of street networks

Corcoran, Padraig ORCID:, Jilani, Musfira, Mooney, Peter and Bertolotto, Michela 2015. Inferring semantics from geometry - the case of street networks. Presented at: ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Seattle, Washington, USA, 3-6 November 2015.

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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.

Item Type: Conference or Workshop Item (Paper)
Date Type: Completion
Status: Unpublished
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date of First Compliant Deposit: 30 March 2016
Date of Acceptance: 1 November 2015
Last Modified: 31 Oct 2022 10:26

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