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Lookup NU author(s): Dr Dimitrios Triantakonstantis,
Professor Stuart Barr
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In order to implement robust climate change adaption and mitigation strategies in cities fine spatial scale information on building stock is required. However, for many cities such information is rarely available. In response, we present a methodology that allows topographic building footprints to be classified to the level of residential spatial topological-building types and corresponding period of construction. The approach developed employs spatial structure and topology to first recognise residential spatial topological types of Detached, Semi-Detached or Terrace. Thereafter, morphological and spatial metrics are employed with multinomial logistic regression to assign buildings to particular periods of construction for use within city-scale impact assessment studies. Overall the system developed performs well for the classification of residential building exemplars for the city of Manchester UK, with an overall accuracy of 83.4%, although with less satisfactory results for the Detached period of construction (76.6%) but excellent accuracies for the Semi-Detached residential buildings (93.0%).
Author(s): Triantakonstantis DP, Barr SL
Editor(s): Gervasi, O; Taniar, D; Murgante, B; Laganà, A; Mun, Y; Gavrilova, M
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: Computational Science and Its Applications: ICCSA 2009, Part 1
Year of Conference: 2009
Publisher: Springer-Verlag Berlin
Library holdings: Search Newcastle University Library for this item
Series Title: Lecture Notes in Computer Science; Theoretical Computer Science and General Issues