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

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

Details

Original languageEnglish
Title of host publication2018 7th European Workshop on Visual Information Processing (EUVIP)
PublisherIEEE
ISBN (Electronic)978-1-5386-6897-9
ISBN (Print)978-1-5386-6898-6
DOIs
Publication statusPublished - Nov 2018
Publication typeA4 Article in a conference publication
EventEuropean Workshop on Visual Information Processing -
Duration: 1 Jan 1900 → …

Publication series

Name
ISSN (Electronic)2471-8963

Conference

ConferenceEuropean Workshop on Visual Information Processing
Period1/01/00 → …

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.

Publication forum classification

Field of science, Statistics Finland