TITLE: IFCNet: A Benchmark Dataset for IFC Entity Classification 

ABSTRACT: Enhancing interoperability and information exchange between domain-specific
software products for BIM is an important aspect in the Architecture,
Engineering, Construction and Operations industry. Recent research started
investigating methods from the areas of machine and deep learning for semantic
enrichment of BIM models. However, training and evaluation of these machine
learning algorithms requires sufficiently large and comprehensive datasets.
This work presents IFCNet, a dataset of single-entity IFC files spanning a
broad range of IFC classes containing both geometric and semantic information.
Using only the geometric information of objects, the experiments show that
three different deep learning models are able to achieve good classification
performance.
Enhancing interoperability and information exchange between domain-specific
software products for BIM is an important aspect in the Architecture,
Engineering, Construction and Operations industry. Recent research started
investigating methods from the areas of machine and deep learning for semantic
enrichment of BIM models. However, training and evaluation of these machine
learning algorithms requires sufficiently large and comprehensive datasets.
This work presents IFCNet, a dataset of single-entity IFC files spanning a
broad range of IFC classes containing both geometric and semantic information.
Using only the geometric information of objects, the experiments show that
three different deep learning models are able to achieve good classification
performance.
IFCNet: A Benchmark Dataset for IFC Entity Classification
http://arxiv.org/abs/2106.09712v1