Automated Facility Enumeration for Building Compliance Checking using Door Detection and Large Language Models
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914057913630720 |
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| author | Zhang, Licheng Le, Bach Akhtar, Naveed Ngo, Tuan |
| author_facet | Zhang, Licheng Le, Bach Akhtar, Naveed Ngo, Tuan |
| contents | Building compliance checking (BCC) is a critical process for ensuring that constructed facilities meet regulatory standards. A core component of BCC is the accurate enumeration of facility types and their spatial distribution. Despite its importance, this problem has been largely overlooked in the literature, posing a significant challenge for BCC and leaving a critical gap in existing workflows. Performing this task manually is time-consuming and labor-intensive. Recent advances in large language models (LLMs) offer new opportunities to enhance automation by combining visual recognition with reasoning capabilities. In this paper, we introduce a new task for BCC: automated facility enumeration, which involves validating the quantity of each facility type against statutory requirements. To address it, we propose a novel method that integrates door detection with LLM-based reasoning. We are the first to apply LLMs to this task and further enhance their performance through a Chain-of-Thought (CoT) pipeline. Our approach generalizes well across diverse datasets and facility types. Experiments on both real-world and synthetic floor plan data demonstrate the effectiveness and robustness of our method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_17283 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automated Facility Enumeration for Building Compliance Checking using Door Detection and Large Language Models Zhang, Licheng Le, Bach Akhtar, Naveed Ngo, Tuan Computer Vision and Pattern Recognition Artificial Intelligence Emerging Technologies Building compliance checking (BCC) is a critical process for ensuring that constructed facilities meet regulatory standards. A core component of BCC is the accurate enumeration of facility types and their spatial distribution. Despite its importance, this problem has been largely overlooked in the literature, posing a significant challenge for BCC and leaving a critical gap in existing workflows. Performing this task manually is time-consuming and labor-intensive. Recent advances in large language models (LLMs) offer new opportunities to enhance automation by combining visual recognition with reasoning capabilities. In this paper, we introduce a new task for BCC: automated facility enumeration, which involves validating the quantity of each facility type against statutory requirements. To address it, we propose a novel method that integrates door detection with LLM-based reasoning. We are the first to apply LLMs to this task and further enhance their performance through a Chain-of-Thought (CoT) pipeline. Our approach generalizes well across diverse datasets and facility types. Experiments on both real-world and synthetic floor plan data demonstrate the effectiveness and robustness of our method. |
| title | Automated Facility Enumeration for Building Compliance Checking using Door Detection and Large Language Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Emerging Technologies |
| url | https://arxiv.org/abs/2509.17283 |