Digital Invasions: From Point Clouds to Historical Building Object Modeling (H-Bom) of a Unesco WHL Site

Chiabrando, Filiberto; Turco, Massimiliano Lo; Santagati, Cettina

doi:10.5194/isprs-archives-xlii-2-w3-171-2017

Cited by 20 publications

(12 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Anton et al [18] combine both optical (OS) and Terrestrial Laser Scanners (TLS) to produce accurate building information models that can be subsequently used for evaluating the quality of the produced complex geometries. This complexity of Scan-to-HBIM tasks has encouraged the generation of parametric models and libraries containing architectural elements [19].…”

Section: Processing Reality Capture Data For Supporting Surveyorsmentioning

confidence: 99%

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

Valero

Förster

Bosché

et al. 2019

Automation in Construction

116

View full text Add to dashboard Cite

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.

show abstract

Section: Processing Reality Capture Data For Supporting Surveyorsmentioning

confidence: 99%

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

Valero

Förster

Bosché

et al. 2019

Automation in Construction

116

View full text Add to dashboard Cite

show abstract

“…Once sufficient data have been collected, the object can be surveyed, thus collecting direct data on the specific case study. The practice of modeling the object under examination by photogrammetry is well established, as the potential of this method has also been analyzed several times with low-cost instruments (Chiabrando et al 2017), especially when compared with instruments such as laser scanners, which are often put in the background from much cheaper cameras. Results of the photogrammetry are the clouds of points (sparse or dense) and the mesh surfaces, with textures or not.…”

Section: Figure 1 Classic Workflow For H-bim With External Modeling mentioning

confidence: 99%

Digital Survey and Algorithmic Modeling in Hbim. Towards a Library of Complex Construction Elements

Bagnolo

Argiolas

Cuccu

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

<p><strong>Abstract.</strong> In the study of built heritage, the introduction of BIM models provides the advantage of a set of data that can be shared between different platforms. Despite the continuous progress in research, the modeling processes of complex construction elements, typical of historical architectures, always require a certain attention and care that involve considerable investments in terms of both resources and time. In this paper we present the first results of an ongoing research aimed at considering possible methods that can allow a simplification of the modeling processes of elements of historical architectures in the BIM environment. In particular, the research aims to explore the possibilities offered by the algorithmic modeling of complex construction elements. In the study of historical architecture, an enough recurring theme concerns those elements that can be traced back to the principles of architectural orders. In the survey of historical architecture, a quite recurring case study concerns those elements or parts of building ruled by the principles of architectural orders. One of the first elements taken into consideration was that of the column that, in its articulation in base, shaft and capital, offers three different levels of complexity of the modeling process very suitable for the research path. Starting with a collection of data acquired from digital photogrammetric modeling, we considered a initial set of different case studies that allowed us to conduct a first working hypothesis by embracing a quite wide range of possible variations starting from a basic column.</p>

show abstract

“…transition from analogue metric cameras to digital non-metric cameras in photogrammetry). That being said, the laser scanning technology has more or less become the standard solution in heritage documentation (Chiabrando et al, 2017;Herbig et al, 2019;Lachat et al, 2017;Pöchtrager et al, 2018). The technology has been around since 1980s and was a revolutionary technology in the domain of 3D mapping.…”

Section: Related Workmentioning

confidence: 99%

Technical Considerations in Low-Cost Heritage Documentation

Murtiyoso

Grussenmeyer

Suwardhi

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. The use of photogrammetry in 3D heritage documentation has matured over the recent years. In the same time, many types of sensors have also been developed in the field of imaging. While photogrammetry is considered as a low-cost alternative to TLS, several options exist in terms of sensor type with trade-offs between price, ease of use, and quality of resolution. Nevertheless, a proper knowledge on the acquisition and processing is still required to generate acceptable results. This paper aims to compare three photogrammetric sensors, namely a classical DSLR camera, a drone, and a spherical 360° camera in documenting heritage sites. Main comparison points include quality of the bundle adjustment and quality of the dense point cloud. However, an important point of the paper is also to determine whether a sensor at a given cost and effort is enough for documentation purposes. A TLS point cloud data was used as a common reference, as well as control and check points issued from geodetic surveying. In the aftermath of the comparison, several technical suggestions and recommendations were proposed as regards to the use of each sensor.

show abstract

Digital Invasions: From Point Clouds to Historical Building Object Modeling (H-Bom) of a Unesco WHL Site

Cited by 20 publications

References 20 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

Digital Survey and Algorithmic Modeling in Hbim. Towards a Library of Complex Construction Elements

Technical Considerations in Low-Cost Heritage Documentation

Contact Info

Product

Resources

About