| id |
ecaade2024_29 |
| authors |
Brama, Haya; Dalach, Agata; Grinshpoun, Tal; Dortheimer, Jonathan |
| year |
2024 |
| title |
Towards a Robust Evaluation Framework for Generative Urban Design |
| source |
Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 1, pp. 529–538 |
| doi |
https://doi.org/10.52842/conf.ecaade.2024.1.529
|
| summary |
This paper critically reviews the evaluation methods employed in the Generative Urban Design (GUD) literature. The review reveals various evaluation methods, including human-based, performance-based, and statistical evaluation. An analysis of the evaluation methods shows that each approach has limitations, and none fully addresses the unique challenges of evaluating GUD. The paper concludes that more robust and comprehensive evaluation methods are needed for GUD. The findings of this study have implications for GUD researchers, providing them with a critical understanding of the strengths and limitations of current evaluation methods and suggesting directions for future research. |
| keywords |
Machine-Learning, Generative Urban Design, Deep Learning, GAN, FID score |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (419,026 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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