| id |
acadia23_v2_508 |
| authors |
Koehler, Daniel; liu, Zidong |
| year |
2023 |
| title |
Exploring Building Typologies and their Socioeconomic Contexts: Compositional Insights from Large-Scale-Text-to-Image Models |
| source |
ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 2: Proceedings of the 43rd Annual Conference for the Association for Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9891764-0-3]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 508-517. |
| summary |
This study utilizes large-scale-text-to-image (LLI) models to investigate possibilities to describe building types data-centric. With the introduction of ""data-centric typologies"" we hope to challenge traditional architectural classification systems, while reviving type as an architectural strategy to link socio-economic contexts to the physical form of a place. By examining artificial intelligence (AI)-generated images of various city buildings, the research explores compositional characteristics, realism, and model limitations. We generated and segmented a synthetic dataset of 15,000 images into individual building segments, conducting a statistical analysis of compositional features across 500 cities. Despite dataset biases and limitations, our results indicate that synthetic databases provide a deeper analytical basis than traditional methods. The generated dataset alone paints forensic landscapes of locales that are not typically showcased. Particularly from a pedagogical perspective, data-centric investigations can serve as a valuable tool for illustrating the diversity of cities and living modes. The findings show that socio-economic attributes, like quality of life, are more closely tied to neighborhoods or projects than entire cities. Consequently, architectural typologies are most effective at a human-ori- ented scale, interfacing city with architecture. |
| series |
ACADIA |
| type |
paper |
| email |
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| full text |
 file.pdf (2,880,066 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/12/20 09:13 |
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