An Integrated Platform for LEED Certification Automation Using Computer Vision and LLM-RAG

• URL: http://arxiv.org/abs/2506.00888v1
• Date: Sun, 01 Jun 2025 08:05:35 GMT
• Title: An Integrated Platform for LEED Certification Automation Using Computer Vision and LLM-RAG
• Authors: Jooyeol Lee,
• Abstract summary: This paper presents an automated platform designed to streamline key aspects of LEED certification.<n>The platform integrates a PySide6-based user interface, a review Manager for process orchestration, and multiple analysis engines for credit compliance, energy modeling via EnergyPlus, and location-based evaluation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The Leadership in Energy and Environmental Design (LEED) certification process is characterized by labor-intensive requirements for data handling, simulation, and documentation. This paper presents an automated platform designed to streamline key aspects of LEED certification. The platform integrates a PySide6-based user interface, a review Manager for process orchestration, and multiple analysis engines for credit compliance, energy modeling via EnergyPlus, and location-based evaluation. Key components include an OpenCV-based preprocessing pipeline for document analysis and a report generation module powered by the Gemma3 large language model with a retrieval-augmented generation framework. Implementation techniques - including computer vision for document analysis, structured LLM prompt design, and RAG-based report generation - are detailed. Initial results from pilot project deployment show improvements in efficiency and accuracy compared to traditional manual workflows, achieving 82% automation coverage and up to 70% reduction in documentation time. The platform demonstrates practical scalability for green building certification automation.

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