FOR IMMEDIATE RELEASE

AI Sampling Singapore: Redefining High-Rise Housing Through Generative Artificial Intelligence

AI Sampling Singapore, led by Immanuel Koh and the Artificial-Architecture team, is a groundbreaking digital research project that leverages artificial intelligence to generate new high-rise public housing designs inspired by Singapore's unique urban landscape. Utilizing a custom-built 3D generative adversarial network (3D-GAN), the project was trained on an extensive dataset of local high-rise buildings, enabling the AI to produce novel, plausible, and contextually relevant architectural forms. The project has garnered significant attention, having been exhibited at the Venice Architecture Biennale and Singapore's Arts House, and has now been honored with the Iron A' Design Award 2025.

Unlike traditional architectural design processes, AI Sampling Singapore explores the creative potential of deep generative neural networks to learn both the exterior and interior spatial configurations of Singapore's public housing. The project addresses the challenge of collecting and curating a comprehensive 3D dataset by developing new workflows and annotation tools, ultimately allowing the AI to interpolate and extrapolate among established typologies such as slab, point, and cluster blocks. The result is a series of high-density residential towers-up to 30 floors-each uniquely generated by the AI, yet grounded in the local architectural vernacular.

This ongoing research, initiated in 2021 at a university design lab, demonstrates how artificial intelligence can be harnessed to not only replicate but also innovate within the constraints of local building practices. By training the AI model from scratch with proprietary code and data, the team has shown that it is possible to generate new, regulation-compliant designs without explicit rule-based controls. The project's success is further underscored by its recognition at international design competitions, including WAFX2024, IDA2024, and BLT2024, solidifying its impact on the future of AI-driven architecture.

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