Title :
Quantitative + qualitative information for heritage conservation an open science research for paving ‘collaboratively’ the way to historical-BIM
Author :
Jorge Garcia-Fernandez;Joutsiniemi Anssi;Yushin Ahn;Juan Jose Fernandez
Author_Institution :
School of Architecture, Tampere University of Technology, Finland
Abstract :
Insofar as our cultural heritage (CH) has become not only an economic resource but a key element in defining our identity, its accurate and flexible documentation has emerged as an essential task. The generation of 3D information with physical and functional characteristics is now possible through the connection of survey data with Historical Building Information Modeling (HBIM). However, few studies have focused on the semantic enrichment process of models based on point clouds, especially on the field of cultural heritage. These singularities make the conversion of point cloud to “as-built” HBIM an expensive process from the mathematical and computational viewpoint. At present, there is no software that guarantees automatic and efficient data conversion in architectural or urban contexts. The ongoing research “Documenting and Visualizing Industrial Heritage” is conducted by the School of Architecture, Tampere University of Technology, Finland based on an Open Notebook Reserarch Model. It is focused on advance the knowledge of digital operating environments for the representation and management of historical buildings and sites. On the one hand, the research is advancing in three-dimensional “as-built” modeling based on remote sensing data, while on the other hand is aiming to incorporate more qualitative information based on concepts of production and management in the lifecycle of the built environment. The purpose of this presentation is to discuss the different approaches to date on the HBIM generation chain: from 3D point cloud data collection to semantically enriched parametric models.
Keywords :
"Computational modeling","Context modeling","Europe","Context","Indexes","Analytical models","Data models"
Conference_Titel :
Digital Heritage, 2015
Print_ISBN :
978-1-5090-0254-2
DOI :
10.1109/DigitalHeritage.2015.7419495