SAGC-A68: a space access graph dataset for the classification of spaces and space elements in apartment buildings
This addresses a data gap for researchers and practitioners in building analysis, enabling automated classification to reduce effort and errors, though it is incremental as it provides a new dataset rather than a novel method.
The researchers tackled the lack of datasets for Graph Deep Learning in classifying spaces and space elements in apartment buildings by introducing SAGC-A68, a dataset of access graphs from 68 3D models, and demonstrated its utility by training a graph attention network to predict 28 classes.
The analysis of building models for usable area, building safety, and energy use requires accurate classification data of spaces and space elements. To reduce input model preparation effort and errors, automated classification of spaces and space elements is desirable. A barrier hindering the utilization of Graph Deep Learning (GDL) methods to space function and space element classification is a lack of suitable datasets. To bridge this gap, we introduce a dataset, SAGC-A68, which comprises access graphs automatically generated from 68 digital 3D models of space layouts of apartment buildings. This graph-based dataset is well-suited for developing GDL models for space function and space element classification. To demonstrate the potential of the dataset, we employ it to train and evaluate a graph attention network (GAT) that predicts 22 space function and 6 space element classes. The dataset and code used in the experiment are available online. https://doi.org/10.5281/zenodo.7805872, https://github.com/A2Amir/SAGC-A68.