Machine learning and complex compositional principles in architecture: Application of convolutional neural networks for generation of context-dependent spatial compositions

Abstract

A substantial part of architectural and urban design involves processing of compositional interdependencies and contexts. This article attempts to isolate the problem of spatial composition from the broader category of synthetic image processing. The capacity of deep convolutional neural networks for recognition and utilization of complex compositional principles has been demonstrated and evaluated under three scenarios varying in scope and approach. The proposed method reaches 95.1%–98.5% efficiency in the generation of context-fitting spatial composition. The technique can be applied for the extraction of compositional principles from the architectural, urban, or artistic contexts and may facilitate the design-related decision making by complementing the required expert analysis.