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id ecaade2024_29
authors Brama, Haya; Dalach, Agata; Grinshpoun, Tal; Dortheimer, Jonathan
year 2024
title Towards a Robust Evaluation Framework for Generative Urban Design
doi https://doi.org/10.52842/conf.ecaade.2024.1.529
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
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)
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