Historic Building Information Modelling (HBIM) is a new approach for modelling historic buildings which develops full Building Information Models (BIMs) from remotely sensed data. HBIM consists of a novel library of reusable parametric objects, based on historic architectural data and a system for mapping theses library objects to survey data. This chapter describes the development of a library of parametric objects for HBIM that can be used to model classical architectural elements. Steps towards automating the HBIM process are also described in this chapter. Using concepts from procedural modelling, a new set of rules and algorithms have been developed to automatically combine HBIM library objects and generate different building arrangements by altering parameters. This is a semi-automatic process where the required building structure and objects are first automatically generated and then refined to match survey data. The use of procedural modelling techniques with HBIM library objects introduces automation and speeds up the slow process of plotting library objects to survey data.
ABSTRACT:Abstract Historic Building Information Modelling (HBIM) is a novel prototype library of parametric objects based on historic data and a system of cross platform programmes for mapping parametric objects onto a point cloud and image survey data. The HBIM process begins with remote collection of survey data using a terrestrial laser scanner combined with digital photo modelling. The next stage involves the design and construction of a parametric library of objects, which are based on the manuscripts ranging from Vitruvius to 18th century architectural pattern books. In building parametric objects, the problem of file format and exchange of data has been overcome within the BIM ArchiCAD software platform by using geometric descriptive language (GDL). The plotting of parametric objects onto the laser scan surveys as building components to create or form the entire building is the final stage in the reverse engineering process. The final HBIM product is the creation of full 3D models including detail behind the object's surface concerning its methods of construction and material make-up. The resultant HBIM can automatically create cut sections, details and schedules in addition to the orthographic projections and 3D models (wire frame or textured).
Vegetation mapping, identifying the type and distribution of plant species, is important for analysing vegetation dynamics, quantifying spatial patterns of vegetation evolution, analysing the effects of environmental changes and predicting spatial patterns of species diversity. Such analysis can contribute to the development of targeted land management actions that maintain biodiversity and ecological functions. This paper presents a methodology for 3D vegetation mapping of a coastal dune complex using a multispectral camera mounted on an unmanned aerial system with particular reference to the Buckroney dune complex in Co. Wicklow, Ireland. Unmanned aerial systems (UAS), also known as unmanned aerial vehicles (UAV) or drones, have enabled high-resolution and high-accuracy ground-based data to be gathered quickly and easily on-site. The Sequoia multispectral sensor used in this study has green, red, red edge and near-infrared wavebands, and a regular camer with red, green and blue wavebands (RGB camera), to capture both visible and near-infrared (NIR) imagery of the land surface. The workflow of 3D vegetation mapping of the study site included establishing coordinated ground control points, planning the flight mission and camera parameters, acquiring the imagery, processing the image data and performing features classification. The data processing outcomes included an orthomosaic model, a 3D surface model and multispectral imagery of the study site, in the Irish Transverse Mercator (ITM) coordinate system. The planimetric resolution of the RGB sensor-based outcomes was 0.024 m while multispectral sensor-based outcomes had a planimetric resolution of 0.096 m. High-resolution vegetation mapping was successfully generated from these data processing outcomes. There were 235 sample areas (1 m × 1 m) used for the accuracy assessment of the classification of the vegetation mapping. Feature classification was conducted using nine different classification strategies to examine the efficiency of multispectral sensor data for vegetation and contiguous land cover mapping. The nine classification strategies included combinations of spectral bands and vegetation indices. Results show classification accuracies, based on the nine different classification strategies, ranging from 52% to 75%.
The design and evaluation of virtual learning environments for construction and surveying students is presented in this paper; by combining virtual learning environment and on-site student surveys to model and replicate practice in the architectural heritage sector. The Virtual Learning Environment is enhanced with real live survey projects whereby students collect the data to build virtual historic buildings from onsite surveys using advanced survey equipment. The survey data is modelled in HBIM; Historic Building Information Modelling (HBIM) is currently being developed as a virtual learning tool for construction and surveying students in the Dublin Institute of Technology. HBIM, is a novel solution whereby interactive parametric objects representing architectural elements are constructed from historic data, these elements, including detail behind the scan surface are accurately mapped onto a laser or image based survey. The architectural elements are scripted using a Geometric Descriptive Language GDL. In the case of this project a Virtual Learning Environment is being developed which combines advanced recording and surveying with Building Information Modelling (BIM) to simulate and analyse existing buildings.
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