{"ID":2868284,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.17283","arxiv_id":"2509.17283","title":"Automated Facility Enumeration for Building Compliance Checking using Door Detection and Large Language Models","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2509.17283","url_pdf":"https://arxiv.org/pdf/2509.17283v2","authors":"[\"Licheng Zhang\",\"Bach Le\",\"Naveed Akhtar\",\"Tuan Ngo\"]","published":"2025-09-21T23:41:44Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.ET\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}