Deep learning surrogate models for spatial and visual connectivity
This work addresses a domain-specific challenge for architects and designers by providing faster connectivity analyses, though it is incremental as it applies existing methods to a new application area.
The paper tackled the problem of slow spatial and visual connectivity simulations for workplace layouts by using machine learning to create surrogate models, achieving a significant speed-up in computation times.
Spatial and visual connectivity are important metrics when developing workplace layouts. Calculating those metrics in real-time can be difficult, depending on the size of the floor plan being analysed and the resolution of the analyses. This paper investigates the possibility of considerably speeding up the outcomes of such computationally intensive simulations by using machine learning to create models capable of identifying the spatial and visual connectivity potential of a space. To that end we present the entire process of investigating different machine learning models and a pipeline for training them on such task, from the incorporation of a bespoke spatial and visual connectivity analysis engine through a distributed computation pipeline, to the process of synthesizing training data and evaluating the performance of different neural networks.