Automated site planning using CAIN-GAN model

Abstract

Automated site planning, powered by deep generative methods, excels in creating solutions responsive to exiting city structures but often overlooks user-specific design scenarios, leading to less performative solutions across varied urban contexts. Overcoming this challenge requires integrating domain knowledge and nuances of the built environment to enhance context-awareness in automated site planning. This study therefore proposes the context-aware site planning generative adversarial networks (CAIN-GAN) framework. In the case study of New York City (NYC), CAIN-GAN demonstrates its capability to not only synthesize visually realistic and semantically reasonable design solutions, but also evaluate their performance in urban sustainability for informed decision-making. This context-aware, learning-based, data-driven, and user-guided generation process signifies a pivotal advancement in more performative and tailored design solutions. Future studies will focus on refining the CAIN-GAN framework to accommodate diverse user-centric design needs and enhance human-machine interaction in urban development.