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Linear street extraction using a Conditional Random Field model

Corcoran, Padraig ORCID:, Mooney, Peter and Bertolotto, Michela 2015. Linear street extraction using a Conditional Random Field model. Spatial Statistics 14 (Part C) , pp. 532-545. 10.1016/j.spasta.2015.10.003

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A novel method for extracting linear streets from a street network is proposed where a linear street is defined as a sequence of connected street segments having a shape similar to a straight line segment. Specifically a given street network is modeled as a Conditional Random Field (CRF) where the task of extracting linear streets corresponds to performing learning and inference with respect to this model. The energy function of the proposed CRF model is submodular and consequently exact inference can be performed in polynomial time. This contrasts with traditional solutions to the problem of extracting linear streets which employ heuristic search procedures and cannot guarantee that the optimal solution will be found. The performance of the proposed method is quantified in terms of identifying those types or classes of streets which generally exhibit the characteristic of being linear. Results achieved on a large evaluation dataset demonstrate that the proposed method greatly outperforms the aforementioned traditional solutions.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Additional Information: Available online 24 October 2015
Publisher: Elsevier
ISSN: 2211-6753
Date of First Compliant Deposit: 30 March 2016
Date of Acceptance: 14 October 2015
Last Modified: 06 Nov 2023 20:53

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