AI Sampling Singapore 集合住宅建築 by Immanuel Koh

AI Sampling Singapore 集合住宅建築 by Immanuel Koh is a highly esteemed winner of the very prestigious 2025 A' Design Award 鉄賞 in ジェネラティブ・アルゴリズミック・パラメトリック・AI支援デザイン Competition Category.

鉄ジェネラティブ· 2025

AI Sampling Singapore 集合住宅建築

by Immanuel Koh

Client: Immanuel Koh

A' Design Award 鉄賞 Winner in ジェネラティブ Category

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The Design

About AI Sampling Singapore 集合住宅建築

シンガポールは人工的な都市である。シンガポールに見られる高層建築の3Dデジタルモデルの大規模なデータセットで学習させたカスタム設計のAIモデルは、形態的に妥当で意味的に一貫した構成を生成するだけでなく、新奇で不可思議な建築形態をも想像し始める。それは、スラブ型、ポイント型、クラスター型といった標準的な高層住宅の類型の間を補間し、また外挿していくのである。

The Recognition

About the A' Design Award 鉄賞

AI Sampling Singapore 集合住宅建築は、よく設計された創造物に期待される専門的・産業的要件を満たす優れたデザインに授与される、権威ある評価である A' Design Award 鉄賞の受賞作品です。鉄賞の受賞作品は、制作者の高い技量、高度な専門性、専門家としての理解、創造的な能力を示しています。AI Sampling Singapore 集合住宅建築は、高く評価され、称賛され、愛される、実用的で革新的な創造物であることが期待されます。この A' Design Award 鉄賞を受賞したデザインは、業界のベストプラクティスを統合し、ノウハウと才能に結びついた有能な技術的特性を備えています。AI Sampling Singapore 集合住宅建築は、その徹底ぶりで尊敬され、世界をより良い場所にするための生活の質の向上をもたらすよう設計されています。

Behind the Design

Press Kit

Credits, team members and technical details provided by the designer.

Unique Properties: Is it possible to sample at scale and at high-resolution every single building ever built in a country? If so, can such a dataset be used to train an AI model in generating new yet locally compliant buildings without any explicit regulatory control inputs? The project explores the design agency of deep generative neural networks in learning three-dimensional exteriority and interiority with a redesigned 3D generative adversarial network (3D-GAN) AI model. Winner of WAFX2024, IDA2024 and BLT2024.
Dimensions & Packaging: This is a digital AI x Architecture project. Each generated output is a new high-rise high-density residential public housing of up to 30 floors.
Tags: AI
Production Technology: A custom generative AI model was trained from scratch using proprietary code base and dataset. Specifically, a 3D generative adversarial network (3D-GAN) AI model was trained to learn semantic and spatial configuration of high-rise high-density residential public housing in Singapore.
Design Challenge: The non-existence of 3D models and their corresponding floor plans was the first challenge. New workflow and tools had to be developed to not only collect (i.e., digitally and physically), but create (annotate) and curate (i.e., exploratory data analysis) such high quality dataset to train an AI model from scratch. Technically difficult due to the scalability of similar CAD representation which was overcome via a novel AI latent representation of 3D.
Research: A custom generative AI model was trained from scratch using proprietary code base and dataset. Specifically, a 3D generative adversarial network (3D-GAN) AI model was trained to learn semantic and spatial configuration of high-rise high-density residential public housing in Singapore.
Inspiration: Singapore is an artificial city! Trained with a large dataset of 3D digital models of high-rise buildings found in Singapore, the custom-designed AI model generates not only formally plausible and semantically coherent configurations, but begins to also imagine novel and uncanny architectural forms, interpolating and extrapolating among standard high-rise housing typologies such as the slab, point, and cluster blocks. Project exhibited at Venice Architecture Biennale and Singapore's Arts House.
Project Duration: This is an on-going and longer-term AI x Architecture research being conducted at the university design lab since 2021. It was however only published much later as an official architectural design award entry from 2024 onwards.
Operation Flow: This is a digital AI x Architecture generative research project, while the dataset was digitally collected from existing physical buildings in Singapore.
Patents: Currently no patent.
Team Members: Artificial-Architecture
Image Credits: All credits: Artificial-Architecture