Hand drawing-based daylight analysis using deep learning and augmented reality

Abstract

The utilization of artificial intelligence (AI) in daylight analysis has recently experienced an increase. Its capabilities have been demonstrated through different studies. While using AI for daytime simulation can reduce time, it also presents significant challenges. Whether we utilize the conventional approach of simulating daylight or employ artificial intelligence, the process of going from hand drawing to obtaining the daylight analysis result involves intricate procedures associated with modeling, programming, and integrating AI models. This paper introduces an efficient approach to rapidly predict daylight performance directly from the user's hand-drawn sketches. The core idea is to leverage a deep learning model for predicting directly from hand drawing and augmented reality (AR) to seamlessly display results in an AR environment, overlaying them on the original drawings. This method not only increases user engagement and accessibility but also eliminates the dependency on traditional daylight simulation software and workflow. Artificial neural network (ANN) was exploited using data derived from 3D building models and Useful Daylight Illuminance simulations created by Rhino3D and Honeybee. The color code method, various imaging, and data processing techniques were implemented to ensure compatibility between the hand-drawn input and the simulation model. Unity3D is used as the development platform with the AR engine Vuforia. Notably, the ANN model yields promising results, with R2 ranging from 0.872 to 0.985. This is achieved by efficiently capturing and converting grid-based hand-drawn images into suitable input for the ANN. The obtained results demonstrate the feasibility and emphasize the concept's practicality.

Keywords

• Deep learning (DL)
• Augmented reality (AR)
• Artificial neural network (ANN)
• Daylight analysis
• Hand drawing