An Integrated Platform for LEED Certification Automation Using Computer Vision and LLM-RAG
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.
Keywords
Cite
@article{arxiv.2506.00888,
title = {An Integrated Platform for LEED Certification Automation Using Computer Vision and LLM-RAG},
author = {Jooyeol Lee},
journal= {arXiv preprint arXiv:2506.00888},
year = {2025}
}