# SAGC-A68: a space access graph dataset for the classification of spaces and space elements in apartment buildings

arXiv: https://arxiv.org/abs/2307.04515 · HTML: https://mlsanity.com/paper/2307.04515/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 10 Jul 2023.

## Builds on (most cited first, 6 of 6)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [Visual Analytics and Human Involvement in Machine Learning](https://mlsanity.com/paper/2005.06057/) (2020)
- [Batch Layer Normalization, A new normalization layer for CNNs and RNN](https://mlsanity.com/paper/2209.08898/) (2022)
- [SFS-A68: a dataset for the segmentation of space functions in apartment buildings](https://mlsanity.com/paper/2209.09094/) (2022)

## Abstract (arXiv)

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.