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Classification of Building Information Model (BIM) Structures with Deep Learning

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Classification of Building Information Model (BIM) Structures with Deep Learning. / Lomio, Francesco; Farinha, Ricardo ; Laasonen, Mauri; Huttunen, Heikki.

2018 7th European Workshop on Visual Information Processing (EUVIP). IEEE, 2018.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Lomio, F, Farinha, R, Laasonen, M & Huttunen, H 2018, Classification of Building Information Model (BIM) Structures with Deep Learning. in 2018 7th European Workshop on Visual Information Processing (EUVIP). IEEE, European Workshop on Visual Information Processing, 1/01/00. https://doi.org/10.1109/EUVIP.2018.8611701

APA

Lomio, F., Farinha, R., Laasonen, M., & Huttunen, H. (2018). Classification of Building Information Model (BIM) Structures with Deep Learning. In 2018 7th European Workshop on Visual Information Processing (EUVIP) IEEE. https://doi.org/10.1109/EUVIP.2018.8611701

Vancouver

Lomio F, Farinha R, Laasonen M, Huttunen H. Classification of Building Information Model (BIM) Structures with Deep Learning. In 2018 7th European Workshop on Visual Information Processing (EUVIP). IEEE. 2018 https://doi.org/10.1109/EUVIP.2018.8611701

Author

Lomio, Francesco ; Farinha, Ricardo ; Laasonen, Mauri ; Huttunen, Heikki. / Classification of Building Information Model (BIM) Structures with Deep Learning. 2018 7th European Workshop on Visual Information Processing (EUVIP). IEEE, 2018.

Bibtex - Download

@inproceedings{20fdc02c5faa410495b75c6eb04b7f0d,
title = "Classification of Building Information Model (BIM) Structures with Deep Learning",
abstract = "In this work we study an application of machine learning to the construction industry and we use classical and modern machine learning methods to categorize images of building designs into three classes: Apartment building, Industrial building or Other. No real images are used, but only images extracted from Building Information Model (BIM) software, as these are used by the construction industry to store building designs. For this task, we compared four different methods: the first is based on classical machine learning, where Histogram of Oriented Gradients (HOG) was used for feature extraction and a Support Vector Machine (SVM) for classification; the other three methods are based on deep learning, covering common pre-trained networks as well as ones designed from scratch. To validate the accuracy of the models, a database of 240 images was used. The accuracy achieved is 57{\%} for the HOG + SVM model, and above 89{\%} for the neural networks.",
author = "Francesco Lomio and Ricardo Farinha and Mauri Laasonen and Heikki Huttunen",
year = "2018",
month = "11",
doi = "10.1109/EUVIP.2018.8611701",
language = "English",
isbn = "978-1-5386-6898-6",
publisher = "IEEE",
booktitle = "2018 7th European Workshop on Visual Information Processing (EUVIP)",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Classification of Building Information Model (BIM) Structures with Deep Learning

AU - Lomio, Francesco

AU - Farinha, Ricardo

AU - Laasonen, Mauri

AU - Huttunen, Heikki

PY - 2018/11

Y1 - 2018/11

N2 - In this work we study an application of machine learning to the construction industry and we use classical and modern machine learning methods to categorize images of building designs into three classes: Apartment building, Industrial building or Other. No real images are used, but only images extracted from Building Information Model (BIM) software, as these are used by the construction industry to store building designs. For this task, we compared four different methods: the first is based on classical machine learning, where Histogram of Oriented Gradients (HOG) was used for feature extraction and a Support Vector Machine (SVM) for classification; the other three methods are based on deep learning, covering common pre-trained networks as well as ones designed from scratch. To validate the accuracy of the models, a database of 240 images was used. The accuracy achieved is 57% for the HOG + SVM model, and above 89% for the neural networks.

AB - In this work we study an application of machine learning to the construction industry and we use classical and modern machine learning methods to categorize images of building designs into three classes: Apartment building, Industrial building or Other. No real images are used, but only images extracted from Building Information Model (BIM) software, as these are used by the construction industry to store building designs. For this task, we compared four different methods: the first is based on classical machine learning, where Histogram of Oriented Gradients (HOG) was used for feature extraction and a Support Vector Machine (SVM) for classification; the other three methods are based on deep learning, covering common pre-trained networks as well as ones designed from scratch. To validate the accuracy of the models, a database of 240 images was used. The accuracy achieved is 57% for the HOG + SVM model, and above 89% for the neural networks.

U2 - 10.1109/EUVIP.2018.8611701

DO - 10.1109/EUVIP.2018.8611701

M3 - Conference contribution

SN - 978-1-5386-6898-6

BT - 2018 7th European Workshop on Visual Information Processing (EUVIP)

PB - IEEE

ER -