BETWEEN IMAGES AND BUILT FORM: AUTOMATING THE RECOGNITION OF STANDARDISED BUILDING COMPONENTS USING DEEP LEARNING
BETWEEN IMAGES AND BUILT FORM: AUTOMATING THE RECOGNITION OF STANDARDISED BUILDING COMPONENTS USING DEEP LEARNING
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DOI：
10.5194/isprs-annals-iv-2-w6-123-2019
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发表时间：
2019-08
期刊：
影响因子：
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通讯作者：
C. Pezzica;J. Schroeter;O. Prizeman;C. B. Jones;Paul L. Rosin
中科院分区：
文献类型：
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作者：
C. Pezzica;J. Schroeter;O. Prizeman;C. B. Jones;Paul L. Rosin
抽象的。基于该领域最近的丰富贡献，本文提出了一种最先进的CNN分析方法，用于自动识别现代遗产建筑中的标准化建筑构件。在20世纪初，制造的建筑构件被建筑师广泛宣传为规格。因此，一种跨越各种类型学的标准化形式开始出现。在这个经济和工业化快速发展的时代，许多形式的公共建筑被建立起来。本文旨在展示一种方法，使用深度学习来识别一系列建筑物中的元素的“家族”，以便从当代贸易文献中检索和识别其技术规格。该方法是通过在英国的卡内基公共图书馆，它提供了一个独特的，但无处不在的平台，探索制造的标准建筑构件的自动识别的潜力的情况下说明。加强这一知识基础的目的是利用这些标准化的程度，最初作为一种手段，以告知和支持他们的持续护理，以及许多其他当代建筑。虽然这些图书馆数量众多，但它们都是在地方一级维护的，因此，它们在维护方面面临的共同挑战彼此仍然不了解。此外，本文还提出了一种方法，通过将深度学习应用于各种建筑图像，间接检索与新兴HBIM家族相关的有用指标和语义。
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.