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Between images and built form: automating the recognition of standardised building components using deep learning

Pezzica, Camilla ORCID:, Schroeter, Julien, Prizeman, Oriel ORCID:, Jones, Christopher ORCID: and Rosin, Paul ORCID: 2019. Between images and built form: automating the recognition of standardised building components using deep learning. Presented at: 27th CIPA International Symposium “Documenting the past for a better future”, Avila, Spain, 1-5 September 2019. ISPRS Ann. Photogramm. Remote Sens. , vol.IV-2 Copernicus, pp. 123-132. 10.5194/isprs-annals-IV-2-W6-123-2019

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Building on the richness of recent contributions in the field, this paper presents a state-of-the-art CNN analysis method for automating the recognition of standardised building components in modern heritage buildings. At the turn of the twentieth century manufactured building components became widely advertised for specification by architects. Consequently, a form of standardisation across various typologies began to take place. During this era of rapid economic and industrialised growth, many forms of public building were erected. This paper seeks to demonstrate a method for informing the recognition of such elements using deep learning to recognise ‘families’ of elements across a range of buildings in order to retrieve and recognise their technical specifications from the contemporary trade literature. The method is illustrated through the case of Carnegie Public Libraries in the UK, which provides a unique but ubiquitous platform from which to explore the potential for the automated recognition of manufactured standard architectural components. The aim of enhancing this knowledge base is to use the degree to which these were standardised originally as a means to inform and so support their ongoing care but also that of many other contemporary buildings. Although these libraries are numerous, they are maintained at a local level and as such, their shared challenges for maintenance remain unknown to one another. Additionally, this paper presents a methodology to indirectly retrieve useful indicators and semantics, relating to emerging HBIM families, by applying deep learning to a varied range of architectural imagery.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
T Technology > TH Building construction
Publisher: Copernicus
Funders: AHRC
Date of First Compliant Deposit: 5 September 2019
Date of Acceptance: 19 July 2019
Last Modified: 06 Jan 2023 02:54

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