Advances in the digital recording of cultural monuments

Ogleby, Cliff

doi:10.1016/0924-2716(95)91286-s

Cited by 8 publications

(5 citation statements)

References 4 publications

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“…Within this context, the rapid evolution of reality capture technologies has facilitated the representation of different environments in either printed or digital formats [6]. Among these devices, photographic cameras have been conventionally employed to produce instantaneous 2D illustrations and, more recently, photogrammetric techniques (e.g.…”

Section: Digital Reality Capturementioning

confidence: 99%

Automated defect detection and classification in ashlar masonry walls using machine learning

Valero

Förster

Bosché

et al. 2019

Automation in Construction

124

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Methods employed for surveying buildings for condition have traditionally been reliant upon visual assessment and manual recording. Survey of traditional masonry also ostensibly conforms to this approach but, due to the sheer volume of masonry units composing walls, it is often prohibitively time consuming, exceptionally complex and ultimately costly. Notable features of such survey work for ashlar stone types require each stone to be labelled and overlaid with information relative to condition. Further hindering these already costly operations, it has been shown that the accuracy of reporting, including labelling the manifestation of defects and defect diagnosis, is subjective, depending upon the expertise and experience of those evaluating the fabric. Moving beyond these preliminary survey and reporting stages, this situation gives rise to variable repair and maintenance strategies that can have significant cost implications and can debase fundamental conservation activities. The development of digital technologies, such as terrestrial laser scanning, and advancements in novel computer vision statistical techniques can help produce accurate representation of buildings that can be subsequently rapidly processed, achieving many tangible survey functions with greater inherent objectivity. In this paper, an innovative strategy for automatic detection and classification of defects in digitised ashlar masonry walling is presented. The classification method is based on the use of supervised machine learning algorithms, assisted by surveyors' strategies and expertise to identify defective individual masonry units, through to broader global patterns for groups of stones. The proposed approach has been tested on the main façade of the Chapel Royal in Stirling Castle (Scotland), demonstrating its potential for ashlar masonry forms of wall construction. It is important to recognise that the findings are not limited to this culturally significant building and will be of high value to almost innumerable ashlar-built structures worldwide. The research ultimately attempts to reduce the degree of subjectivity in classifying defects, on a scale and rapidity hitherto beyond traditional project cost constraints. Importantly, it is recognised that through automation more effective utilisation of resources that would have been traditionally spent on survey can be redeployed to support fabric intervention or routine maintenance operations.

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Section: Digital Reality Capturementioning

confidence: 99%

Automated defect detection and classification in ashlar masonry walls using machine learning

Valero

Förster

Bosché

et al. 2019

Automation in Construction

124

View full text Add to dashboard Cite

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“…Their Handbook of Heritage Photogrammetry (Ogleby and Rivett, 1985) was a key text of its day, describing how to conduct a photogrammetric survey for field archaeology. More recently, Ogleby (1995; 1999) has continued to demonstrate the benefits of photogrammetry to a wider archaeological audience, particularly at the Ayutthaya temple in Thailand (Ogleby, 1999).…”

Section: Past Work and Technologiesmentioning

confidence: 99%

The Development And Application Of A Simple Methodology For Recording Rock Art Using Consumer‐Grade Digital Cameras

Chandler¹,

Bryan²,

Fryer³

2007

The Photogrammetric Record

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A simple methodology for recording rock art has been recently developed in Australia and tested on Aboriginal rock art, including both petroglyphs and pictographs (engraved and painted images, respectively). The approach was based on commercial photogrammetric software and consumer‐grade digital cameras, because it was believed that archaeologists, conservators and site managers need simple and cost‐effective methods to record and document rock art. This methodology has been adopted subsequently by the Northumberland and Durham Rock Art Project working in conjunction with English Heritage, to assist in recording 1500 prehistoric engraved panels located across the north‐east of England. Significantly, the fieldwork was carried out by enthusiastic volunteers, willing to sacrifice their weekends to capture imagery suitable for digital photogrammetry. This paper explains briefly how the recording technique was developed in Australia before indicating how expertise and equipment were integrated to allow UK‐based volunteers to carry out data acquisition and, perhaps surprisingly, also the photogrammetry. This will demonstrate the value of mobilising the voluntary sector for heritage recording, which is feasible only if recording methodologies are based on cheap and simple instrumentation.

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“…Laser scanning and photogrammetry [52] have been used extensively to 'record' cultural heritage artefacts (e.g., [53][54][55]) for archival purposes. The techniques utilise a range-finding system that enables the mapping of the artefact into a 'cloud' of 3-D coordinate points.…”

Section: Photogrammetry Techniquesmentioning

confidence: 99%

Deformation and strain measurement techniques for the inspection of damage in works of art

Dulieu-Barton¹,

Dokos²,

Eastop³

et al. 2005

Studies in Conservation

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The engineering techniques used for inspecting structural damage are not widely known in the conservation sector. Techniques are available based on deformation or strain measurement that have the ability to provide quantitative data. This paper reviews currently available techniques, covering point-strain measurements using resistance strain gauges and fibre-optic sensors, as well as full-field optical measurement approaches such as holography, electronic speckle pattern interferometry, photoelastic stress analysis and photogrammetry. The underlying technology of each of the techniques is described for the non-specialist. The relevance of each technique is established from a conservation perspective through accounts of usage. The application of the techniques to a wide range of artwork, including panel paintings, statues, murals and mosaics is described and the results critically reviewed. The paper also provides an insight into possible future applications of the techniques and identifies areas for further investigation.

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Advances in the digital recording of cultural monuments

Cited by 8 publications

References 4 publications

Automated defect detection and classification in ashlar masonry walls using machine learning

Automated defect detection and classification in ashlar masonry walls using machine learning

The Development And Application Of A Simple Methodology For Recording Rock Art Using Consumer‐Grade Digital Cameras

Deformation and strain measurement techniques for the inspection of damage in works of art

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