# Towards an automated AI-based framework for floor plan compliance checks for residential buildings

arXiv: https://arxiv.org/abs/2607.00015 · HTML: https://mlsanity.com/paper/2607.00015/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 91 references that are in the map.
- First posted on arXiv 26 May 2026.

## Builds on (most cited first, 17 of 17)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [A Deep Journey into Super-resolution: A survey](https://mlsanity.com/paper/1904.07523/) (2019)
- [CubiCasa5K: A Dataset and an Improved Multi-Task Model for Floorplan Image Analysis](https://mlsanity.com/paper/1904.01920/) (2019)
- [TopViewRS: Vision-Language Models as Top-View Spatial Reasoners](https://mlsanity.com/paper/2406.02537/) (2024)
- [MSD: A Benchmark Dataset for Floor Plan Generation of Building Complexes](https://mlsanity.com/paper/2407.10121/) (2024)
- [Large Language Model-Driven Code Compliance Checking in Building Information Modeling](https://mlsanity.com/paper/2506.20551/) (2025)
- [CODE-ACCORD: A Corpus of building regulatory data for rule generation towards automatic compliance checking](https://mlsanity.com/paper/2403.02231/) (2024)
- [Room Classification on Floor Plan Graphs using Graph Neural Networks](https://mlsanity.com/paper/2108.05947/) (2021)
- [Vision Language Models Can Parse Floor Plan Maps](https://mlsanity.com/paper/2409.12842/) (2024)
- [FloorplanVLM: A Vision-Language Model for Floorplan Vectorization](https://mlsanity.com/paper/2602.06507/) (2026)
- [LLM-Guided Agentic Floor Plan Parsing for Accessible Indoor Navigation of Blind and Low-Vision People](https://mlsanity.com/paper/2604.23970/) (2026)
- [Knowledge driven Description Synthesis for Floor Plan Interpretation](https://mlsanity.com/paper/2103.08298/) (2021)
- [LayoutGKN: Graph Similarity Learning of Floor Plans](https://mlsanity.com/paper/2509.03737/) (2025)
- [Asymmetric Co-Training for Source-Free Few-Shot Domain Adaptation](https://mlsanity.com/paper/2502.14214/) (2025)
- [Automated Facility Enumeration for Building Compliance Checking using Door Detection and Large Language Models](https://mlsanity.com/paper/2509.17283/) (2025)

## Abstract (arXiv)

To improve residents' well-being in Australia's urban areas, governments have introduced policy reforms such as SEPP65, BADS, and SPP7.3 to enhance apartment design quality. These regulations require precise geometric and spatial analysis to evaluate health-related features, including daylight access, natural ventilation, privacy, and space efficiency. However, compliance checking remains challenging due to its manual, time-intensive nature. Additionally, evolving policies limit scalability for large-scale assessments across thousands of apartments. Existing automated floor plan analysis methods are fragmented and typically focus on single apartments, lacking a unified framework for multi-unit compliance checking. This article explores current advancements in automated floor plan analysis, particularly AI-driven approaches, and highlights key challenges in their practical adoption. To address these gaps, a conceptual framework is proposed for automated compliance checking in multi-apartment buildings. A Large Language Model (LLM) is used within a Rule Engine to convert textual building codes into executable, explainable rules. A Data Extraction Engine segments floor plan images into elements such as walls, rooms, fixtures, text, and symbols, and transforms them into a structured building graph with topological relationships. This structured representation is then evaluated by a Compliance Check Engine, which leverages LLM-generated rules for assessment. The proposed framework offers a scalable, consistent, and transparent approach to automated compliance checking across jurisdictions, supporting efficient enforcement of apartment design standards and promoting healthier, higher-density urban development.