Deep learning surrogate models for spatial and visual connectivity

TL;DR

This paper explores using deep learning models to rapidly estimate spatial and visual connectivity in workplace layouts, aiming to improve real-time analysis efficiency for large floor plans.

Contribution

It introduces a comprehensive pipeline for training neural networks to predict connectivity metrics, integrating custom analysis tools and distributed computation for data synthesis and model evaluation.

Findings

Deep learning models can effectively predict connectivity metrics.

The pipeline enables faster analysis of large floor plans.

Neural networks outperform traditional computation methods in speed.

Abstract

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.