AI Sampling Singapore Primary Function: Boligarkitektur by Immanuel Koh

AI Sampling Singapore Primary Function: Boligarkitektur by Immanuel Koh is a highly esteemed winner of the very prestigious 2025 A' Design Award i Jern in Generativt, Algoritmisk, Parametrisk og AI-assisteret Design Competition Category.

JernGenerativt· 2025

AI Sampling Singapore Primary Function: Boligarkitektur

by Immanuel Koh

Client: Immanuel Koh

A' Design Award i Jern Winner in Generativt Category

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Verified Award Winner

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The Design

About AI Sampling Singapore Primary Function: Boligarkitektur

Book Text: Singapore er en kunstig by. Trænet med et stort datasæt af 3D-digitale modeller af højhusbygninger fundet i Singapore genererer den specialdesignede AI-model ikke kun formelt plausible og semantisk sammenhængende konfigurationer, men begynder også at forestille sig nye og uhyggelige arkitektoniske former, idet den interpolerer og ekstrapolerer mellem standardtypologier for højhusbyggeri såsom plade-, punkt- og klyngeblokke.

The Recognition

About the A' Design Award i Jern

AI Sampling Singapore Primary Function: Boligarkitektur er vinder af A' Design Award i Jern, en prestigefyldt anerkendelse, der tildeles gode designs, som opfylder de professionelle og industrielle krav, man forventer af veldesignede skabelser. Jernvinderne viser stor dygtighed, høj specialisering, ekspertforståelse og kreativ kapacitet hos deres skabere. AI Sampling Singapore Primary Function: Boligarkitektur forventes at være en højt estimeret, beundret og kær skabelse, der er praktisk og innovativ. Dette design, der har vundet A' Design Award i Jern, integrerer branchens bedste praksis og har kompetente tekniske egenskaber knyttet til knowhow og talent. AI Sampling Singapore Primary Function: Boligarkitektur respekteres for sin grundighed og er designet til at levere forbedringer af livskvaliteten, der er med til at gøre verden til et bedre sted.

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