nach oben

Erschienen in:

2019 | OriginalPaper | Buchkapitel

Towards the Semantic Enrichment of Existing Online 3D Building Geometry to Publish Linked Building Data

verfasst von : Maarten Bassier, Mathias Bonduel, Jens Derdaele, Maarten Vergauwen

Erschienen in: Knowledge Graphs and Semantic Web

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Currently, existing online 3D databases each have their own structure according to their own needs. Additionally, the majority of online content only has limited semantics. With the advent of Semantic Web technologies, the opportunity arises to semantically enrich the information in these databases and make it widely accessible and queryable. The goal is to investigate whether online 3D content from different repositories can be processed by a single algorithm to produce the desired semantics. The emphasis of this work is on extracting building components from generic 3D building geometry and publish it as Linked Building Data.

An interpretation framework is proposed that takes as input any building mesh and outputs its components. More specifically, we use pretrained Support Vector Machines to classify the separate meshes derived from each 3D model. As a preliminary test case, realistic examples from several repositories are processed. The test results depict that, even though the building content originates from different sources and was not modeled according to any standards, it can be processed by a single machine learning application. As a result, building geometry in online repositories can be semantically enriched with component information according to classes from Linked Data ontologies such as BOT and PRODUCT. This is an important step towards making the implicit content of geometric models queryable and linkable over the Web.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Automated Large Geographic Ontologies Generation Method from Spatial Databases

Nächstes Kapitel A Method for Automatically Generating Schema Diagrams for OWL Ontologies

https://w3id.org/bot#.

https://github.com/w3c-lbd-cg/product.

https://w3id.org/fog#.

Anand, A., Koppula, H.S., Joachims, T., Saxena, A.: Contextually guided semantic labeling and search for 3D point clouds. Int. J. Robot. Res. 32(1), 19–34 (2012). https://doi.org/10.1177/0278364912461538CrossRef

Armeni, I., et al.: Joint 2D–3D-Semantic Data for Indoor Scene Understanding. arXiv:1702.01105 [cs.CV] (2017), http://arxiv.org/abs/1702.01105

Bassier, M., Van Genechten, B., Vergauwen, M., Genechten, B.V., Vergauwen, M.: Classification of sensor independent point cloud data of buildingobjects using random forests. J. Build. Eng. 21, 1–10 (2018). https://doi.org/10.1016/j.jobe.2018.04.027CrossRef

Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Berlin (2006)MATH

Bonduel, M., Oraskari, J., Pauwels, P., Vergauwen, M., Klein, R.: The IFC to linked building data converter - current status. In: 6th Linked Data in Architecture and Construction Workshop, London (2018)

Criminisi, A., Shotton, J.: Decision Forests for Computer Vision and Medical Image Analysis. Springer, Heidelberg (2013). https://doi.org/10.1007/978-1-4471-4929-3CrossRef

Hohmann, B., Havemann, S., Krispel, U., Fellner, D.: A GML shape grammar for semantically enriched 3D building models. Comput. Graph. 34, 322–334 (2010). https://doi.org/10.1016/j.cag.2010.05.007CrossRef

Lu, W., Qin, Y., Liu, X., Huang, M., Zhou, L., Jiang, X.: Computer-aided design enriching the semantics of variational geometric constraint data withontology. Comput. Aided Des. 63, 72–85 (2015). https://doi.org/10.1016/j.cad.2014.12.008MathSciNetCrossRef

Maxwell, A.E., Warner, T.A., Fang, F.: Implementation of machine-learning classification in remote sensing: an applied review. Int. J. Remote Sens. 39(9), 2784–2817 (2018). https://doi.org/10.1080/01431161.2018.1433343CrossRef

10.

Niemeyer, J., Rottensteiner, F., Soergel, U.: Contextual classification of lidar data and building object detection in urban areas. ISPRS J. Photogramm. Remote Sens. 87, 152–165 (2014). https://doi.org/10.1016/j.isprsjprs.2013.11.001. http://linkinghub.elsevier.com/retrieve/pii/S0924271613002359CrossRef

11.

Nikoohemat, S., Peter, M., Oude Elberink, S., Vosselman, G.: Exploiting indoor mobile laser scanner trajectories for semantic interpretation of point clouds. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. IV–2/W4v, 355–362 (2017). https://doi.org/10.5194/isprs-annals-IV-2-W4-355-2017. https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2-W4/355/2017/CrossRef

12.

Pauwels, P., Terkaj, W.: Express to OWL for construction industry: towards are commendable and usable ifcOWL ontology. Autom. Constr. 63, 100–133 (2016). https://doi.org/10.1016/j.autcon.2015.12.003CrossRef

13.

Pauwels, P., Zhang, S., Lee, Y.C.: Semantic web technologies in AEC industry: a literature overview. Autom. Constr. 73, 145–165 (2017). https://doi.org/10.1016/j.autcon.2016.10.003CrossRef

14.

Rasmussen, M.H., Pauwels, P., Lefrançois, M., Schneider, G.F., Hviid, C.A., Karshøj, J.: Recent changes in the building topology ontology. In: 5th Linked Data in Architecture and Construction (LDAC2017) Workshop (2017)

15.

Terkaj, W., Schneider, G.F., Pauwels, P.: Reusing domain ontologies in linked building data: the case of building automation and control. In: CEUR Workshop Proceedings 2050 (2017)

16.

Terkaj, W., Šojić, A.: Ontology-based representation of IFC EXPRESS rules: an enhancement of the ifcOWL ontology. Autom. Constr. 57, 188–201 (2015). https://doi.org/10.1016/j.autcon.2015.04.010CrossRef

17.

Törmä, S.: Semantic linking of building information models. In: Proceedings - 2013 IEEE 7th International Conference on Semantic Computing, ICSC 2013, pp. 412–419 (2013). https://doi.org/10.1109/ICSC.2013.80

18.

Wolf, D., Prankl, J., Vincze, M.: Fast semantic segmentation of 3D point clouds using a dense CRF with learned parameters. In: IEEE International Conference on Robotics and Automation (ICRA) (2015)

19.

Xiong, X., Adan, A., Akinci, B., Huber, D.: Automatic creation of semantically rich 3D building models from laser scanner data. Autom. Constr. 31, 325–337 (2013). https://doi.org/10.1016/j.autcon.2012.10.006. http://linkinghub.elsevier.com/retrieve/pii/S0926580512001732CrossRef

20.

Xiong, X., Huber, D.: Using context to create semantic 3D models of indoor environments. In: Procedings of the British Machine Vision Conference 2010, pp. 45.1–45.11 (2010). https://doi.org/10.5244/C.24.45, http://www.bmva.org/bmvc/2010/conference/paper45/index.html

Titel: Towards the Semantic Enrichment of Existing Online 3D Building Geometry to Publish Linked Building Data
verfasst von: Maarten Bassier
Mathias Bonduel
Jens Derdaele
Maarten Vergauwen
Verlag: Springer International Publishing
Buch: Knowledge Graphs and Semantic Web
Print ISBN: 978-3-030-21394-7

Electronic ISBN: 978-3-030-21395-4

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-21395-4_10

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner