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Abstract
Nowadays, cultural and historical built heritage can be more effectively preserved, val-orised and documented using advanced geospatial technologies. In such a context, there is a major issue concerning the automation of the process and the extraction of useful information from a huge amount of spatial information acquired by means of advanced survey techniques (i.e., highly detailed LiDAR point clouds). In particular, in the case of historical built heritage (HBH) there are very few effective efforts. Therefore, in this paper, the focus is on establishing the connections between semantic and geometrical information in order to generate a parametric, structured model from point clouds using ontology as an effective approach for the formal conceptualisation of application domains. Hence, in this paper, an ontological schema is proposed to structure HBH representations, starting with international standards, vocabularies, and ontologies (CityGML-Geography Markup Language, International Committee for Documentation conceptual reference model (CIDOC-CRM), Industry Foundation Classes (IFC), Getty Art and Architecture Thesaurus (AAT), as well as reasoning about morphology of historical centres by analysis of real case studies) to represent the built and architecture domain. The validation of such schema is carried out by means of its use to guide the segmentation of a LiDAR point cloud from a castle, which is later used to generate parametric geometries to be used in a historical building information model (HBIM).
Original language | English |
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Article number | 2813 |
Number of pages | 22 |
Journal | Applied Sciences (Switzerland) |
Volume | 11 |
Issue number | 6 |
DOIs | |
Publication status | Published - 2021 |
Keywords
- Conceptualisation
- HBH (historical built heritage)
- HBIM (historical building modelling)
- Ontology
- Parametric models
- Point clouds
- Semantic segmentation
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- 1 Finished
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LEaDing Fellows
Gutierrez, A., Dols Perez, A., Bae, D., Sahoo, H., Wang, W., Lam, K. L., Raimondo, A., Steffelbauer, D. B., Lesne, E. L., Ragno, E., Amador, G. J., Šiaudinyte, L., Sand, M., Robinson Garcia, N., Abil, Z., Purkarthofer, E., Noardo, F., Tasić, J. K., Marin, L., Angeloni, L., loddo, M., Stockill, R. H. J., Franklin, S. W., Hensen, B. J., Dennis, M. J., Afroza Islam, S. T., Kim, T., Manzaneque Garcia, T., Tiringer, U., Marques Penha, F., Esteban Jurado, C., Timmermans, E., McCrum, I. T., Pool, F., Forn-Cuní, G., Will, G., Barrett, H. E., Everett, J. A. C., Kostenzer, J., Luksenburg, J., Hirvasniemi, J., Desai, J., Ruibal, P., Albury, N. J., March, R., Eichengreen, A., Muok, A. R., Cochrane, A., Ravesteijn, B., Riumalló Herl, C. J., Meeusen, C., Biaggi, C., Granger, C., Cecil, C., Fosch Villaronga, E., Sánchez López, E. S., Loehrer, E., da Costa Gonçalves, F., Giardina, F., Wu, H., Gleitz, H. & Khatri, I.
2/01/17 → 1/05/22
Project: Research