# Room Classification on Floor Plan Graphs using Graph Neural Networks

arXiv: https://arxiv.org/abs/2108.05947 · HTML: https://mlsanity.com/paper/2108.05947/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 12 Aug 2021.

## Builds on (most cited first, 9 of 9)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [DeepGCNs: Can GCNs Go as Deep as CNNs?](https://mlsanity.com/paper/1904.03751/) (2019)
- [Topology Adaptive Graph Convolutional Networks](https://mlsanity.com/paper/1710.10370/) (2017)
- [House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation](https://mlsanity.com/paper/2003.06988/) (2020)
- [Deep Floor Plan Recognition Using a Multi-Task Network with Room-Boundary-Guided Attention](https://mlsanity.com/paper/1908.11025/) (2019)
- [Extracting Semantic Indoor Maps from Occupancy Grids](https://mlsanity.com/paper/2002.08348/) (2020)

## Built on by (most cited first, 5 of 5)

- [ResPlan: A Large-Scale Vector-Graph Dataset of 17,000 Residential Floor Plans](https://mlsanity.com/paper/2508.14006/) (2025)
- [2SFGL: A Simple And Robust Protocol For Graph-Based Fraud Detection](https://mlsanity.com/paper/2310.08335/) (2023)
- [The scope for AI-augmented interpretation of building blueprints in commercial and industrial property insurance](https://mlsanity.com/paper/2205.01671/) (2022)
- [Towards an automated AI-based framework for floor plan compliance checks for residential buildings](https://mlsanity.com/paper/2607.00015/) (2026)
- [ModRes: A modification of the ResPlan dataset ready for floor plan analysis and graph machine learning](https://mlsanity.com/paper/2610.17178/) (2026)

## Abstract (arXiv)

We present our approach to improve room classification task on floor plan maps of buildings by representing floor plans as undirected graphs and leveraging graph neural networks to predict the room categories. Rooms in the floor plans are represented as nodes in the graph with edges representing their adjacency in the map. We experiment with House-GAN dataset that consists of floor plan maps in vector format and train multilayer perceptron and graph neural networks. Our results show that graph neural networks, specifically GraphSAGE and Topology Adaptive GCN were able to achieve accuracy of 80% and 81% respectively outperforming baseline multilayer perceptron by more than 15% margin.