Volume IV-2/W6
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W6, 123–132, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W6-123-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W6, 123–132, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W6-123-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  21 Aug 2019

21 Aug 2019

BETWEEN IMAGES AND BUILT FORM: AUTOMATING THE RECOGNITION OF STANDARDISED BUILDING COMPONENTS USING DEEP LEARNING

C. Pezzica1, J. Schroeter2, O. E. Prizeman1, C. B. Jones2, and P. L. Rosin2 C. Pezzica et al.
  • 1Welsh School of Architecture, Cardiff University, King Edward VII Avenue, Cardiff, UK
  • 2School of Computer Science & Informatics, Cardiff University, The Parade, Cardiff, UK

Keywords: Object Recognition, HBIM, Image Classification, Deep Learning, CNN, Modern Heritage Conservation, Carnegie Libraries, Specifications

Abstract. 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.