Facade Style Mixing Using Artificial Intelligence for Urban Infill

Abstract

Artificial intelligence and machine learning, in particular, have made rapid advances in image processing. However, their incorporation into architectural design is still in its early stages compared to other disciplines. Therefore, this paper addresses the development of an integrated bottom–up digital design approach and describes a research framework for incorporating the deep convolutional generative adversarial network (GAN) for early stage design exploration and the generation of intricate and complex alternative facade designs for urban interiors. In this paper, a novel facade design is proposed using the architectural style, size, scale, and openings of two adjacent buildings as references to create a new building design in the same neighborhood for urban infill. This newly created building contains the outline, style and shape of the two main buildings. A 2D building design is generated as an image, where (1) neighboring buildings are imported as a reference using the cell phone and (2) iFACADE decodes their spatial neighborhood. It is illustrated that iFACADE will be useful for designers in the early design phase to create new facades in relation to existing buildings in a short time, saving time and energy. Moreover, building owners can use iFACADE to show their preferred architectural facade to their architects by mixing two building styles and creating a new building. Therefore, it is presented that iFACADE can become a communication platform in the early design phases between architects and builders. The initial results define a heuristic function for generating abstract facade elements and sufficiently illustrate the desired functionality of the prototype we developed.