AI Sampling Singapore Primary Function: ᱚᱲᱟᱜ ᱜᱷᱟᱰᱟᱣ ᱠᱟᱹᱢᱤ by Immanuel Koh is a highly esteemed winner of the very prestigious 2025 A' Design Award ᱞᱚᱦᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ in ᱡᱮᱱᱟᱨᱮᱴᱤᱵ Competition Category.
ᱞᱚᱦᱟᱡᱮᱱᱟᱨᱮᱴᱤᱵ· 2025
AI Sampling Singapore Primary Function: ᱚᱲᱟᱜ ᱜᱷᱟᱰᱟᱣ ᱠᱟᱹᱢᱤ
by Immanuel Koh
Client: Immanuel Koh
A' Design Award ᱞᱚᱦᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ Winner in ᱡᱮᱱᱟᱨᱮᱴᱤᱵ Category
Verified Award Winner
Verify Award Status for #170593The Design
About AI Sampling Singapore Primary Function: ᱚᱲᱟᱜ ᱜᱷᱟᱰᱟᱣ ᱠᱟᱹᱢᱤ
Book Text: ᱥᱤᱝᱜᱟᱯᱩᱨ ᱫᱚ ᱢᱤᱫ ᱵᱟᱱᱟᱣᱟᱱ ᱥᱚᱦᱚᱨ ᱠᱟᱱᱟ। ᱥᱤᱝᱜᱟᱯᱩᱨ ᱨᱮ ᱧᱟᱢ ᱚᱠᱚᱭ ᱩᱥᱩᱞ ᱚᱲᱟᱜ ᱠᱚᱣᱟᱜ ᱳᱨ 3D ᱰᱤᱡᱤᱴᱟᱞ ᱢᱳᱰᱮᱞ ᱠᱚᱣᱟᱜ ᱢᱟᱨᱟᱝ ᱰᱟᱴᱟᱥᱮᱴ ᱥᱟᱱᱟᱢ ᱛᱮ ᱴᱨᱮᱱᱤᱝ ᱦᱩᱭ ᱠᱟᱱᱟ, ᱱᱤᱡᱮ ᱰᱤᱡᱟᱭᱱ ᱦᱩᱭ ᱟᱠᱟᱱ AI ᱢᱳᱰᱮᱞ ᱫᱚ ᱪᱮᱫ ᱠᱷᱟᱱ ᱨᱩᱯ ᱨᱮ ᱢᱟᱱᱟᱣᱟᱱ ᱟᱨ ᱢᱟᱱᱮ ᱨᱮ ᱢᱤᱫᱴᱟᱝ ᱠᱚᱱᱯᱷᱤᱜᱩᱨᱮᱥᱟᱱ ᱠᱚ ᱵᱟᱝ ᱛᱮᱭᱟᱨ ᱮᱫᱟ, ᱢᱮᱱᱠᱷᱟᱱ ᱱᱟᱶᱟ ᱟᱨ ᱟᱪᱷᱟᱨᱡ ᱜᱷᱟᱰᱟᱣ ᱨᱩᱯ ᱠᱚ ᱦᱚᱸ ᱯᱟᱦᱟᱴᱟ ᱮᱦᱚᱵ ᱮᱫᱟ, ᱢᱟᱱᱚᱠ ᱩᱥᱩᱞ ᱚᱲᱟᱜ ᱴᱟᱭᱯᱳᱞᱳᱡᱤ ᱠᱚ ᱡᱮᱞᱮᱠᱟ ᱥᱞᱟᱵ, ᱯᱳᱭᱤᱱᱴ, ᱟᱨ ᱠᱞᱟᱥᱴᱟᱨ ᱵᱞᱚᱠ ᱠᱚ ᱛᱟᱞᱟ ᱨᱮ ᱤᱱᱴᱟᱨᱯᱳᱞᱮᱥᱟᱱ ᱟᱨ ᱮᱠᱥᱴᱨᱟᱯᱳᱞᱮᱥᱟᱱ ᱠᱟᱛᱮ।
The Recognition
About the A' Design Award ᱞᱚᱦᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ
AI Sampling Singapore Primary Function: ᱚᱲᱟᱜ ᱜᱷᱟᱰᱟᱣ ᱠᱟᱹᱢᱤ ᱫᱚ A' Design Award ᱞᱚᱦᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ ᱧᱟᱢ ᱟᱠᱟᱱᱟ, ᱱᱚᱶᱟ ᱫᱚ ᱵᱷᱟᱞᱟᱭ ᱰᱤᱡᱟᱭᱤᱱ ᱠᱚ ᱞᱟᱹᱜᱤᱫ ᱮᱢ ᱟᱠᱟᱱ ᱥᱚᱢᱟᱱ ᱠᱟᱱᱟ᱾ ᱱᱚᱶᱟ ᱠᱚ ᱫᱚ ᱰᱤᱡᱟᱭᱤᱱᱟᱨ ᱠᱚ ᱨᱮᱭᱟᱜ ᱵᱷᱟᱵᱱᱟ ᱟᱨ ᱵᱷᱟᱞᱟᱭ ᱠᱟᱢᱤ ᱡᱷᱟᱞᱠᱟᱣ ᱠᱚ᱾ AI Sampling Singapore Primary Function: ᱚᱲᱟᱜ ᱜᱷᱟᱰᱟᱣ ᱠᱟᱹᱢᱤ ᱫᱚ ᱠᱚᱞᱟ, ᱣᱤᱜᱭᱟᱱ, ᱰᱤᱡᱟᱭᱤᱱ ᱟᱨ ᱴᱮᱠᱱᱚᱞᱚᱡᱤ ᱨᱮᱭᱟᱜ ᱥᱤᱢᱟ ᱵᱟᱲᱦᱟᱣ ᱟᱨ ᱫᱩᱱᱤᱭᱟ ᱵᱷᱟᱞᱟᱭ ᱞᱟᱹᱜᱤᱫ ᱜᱚᱲᱚ ᱮᱢ ᱠᱟᱱᱟ᱾
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
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