Architectural Space Recognition from Blueprints Using Machine Learning-Based Semantic Segmentation

Abstract

Architectural blueprints have been providing fundamental processes for urban planning, construction, and interior design for professionals. It contains a visual representation of building plans and specifications in the construction and design industry. However, for the non-expert, it is difficult to understand these complex technical drawings. This is because of the specialized language, symbols, and technical knowledge required to understand architectural blueprints. This challenge often leads to misunderstandings which can extend the construction process and escalate costs. To bridge this knowledge gap, in this research paper, I propose a machine-based blueprint interpretation method using semantic segmentation. The proposed method takes blueprints as input and generates semantic segmentation maps. These maps categorize and isolate distinct architectural areas into predefined classes, including rooms, kitchens, and bathrooms. The proposed machine learning model is tested on a publicly available dataset of architectural blueprints. Through comprehensive quantitative and qualitative assessments, it is shown that the proposed method achieves state-of-the-art performance.