Developing a Robust Training Dataset for AI-Driven Architectural Spatial Layout Generation

Abstract

Recent advancements in AI research, particularly in spatial layout generation, highlight its capacity to enhance human creativity by swiftly providing architects with numerous alternatives during the pre-design phase. The complexity of architectural design data, characterized by multifaceted elements and varying representations, presents significant challenges in creating uniform and robust datasets. This study addresses this challenge by developing a robust training dataset specifically tailored for AI-driven spatial layout generation in architecture. An algorithm capable of extracting spatial relationship diagrams from raster-based floor plan images and converting them into vector-based data was introduced. Through extensive web crawling, a dataset comprising 10,000 data rows, categorized into 21 classes and three spatial relationship categories, was collected. When tested with the You-Only-Look-Once (YOLO) model, the detection rate was 99%, the mean average precision was 85%, and the MIoU was 74.2%. The development of this robust training dataset holds significant potential to advance knowledge-based artificial intelligence design automation studies, paving the way for further innovation in architectural design.

Keywords

• training dataset
• architectural spatial layout generation
• floor plan detection
• spatial relationship diagrams
• YOLO model