| id |
caadria2022_313 |
| authors |
Raanan, Noam, Yoffe, Hatzav and Grobman, Jacob |
| year |
2022 |
| title |
A Machine Learning Evaluation Method for Sustainability Evaluation: The Case of Neighbourhoods' Design |
| doi |
https://doi.org/10.52842/conf.caadria.2022.1.283
|
| source |
Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 283-291 |
| summary |
This paper proposes a framework for machine learning to evaluate landscape design. In this study, we measured key performance indicators of landscape-development plans using a convolutional neural network (CNN) approach to predict the performance level of the design. The model used 3749 performance evaluations from 36 professionals, covering six sustainability criteria in 32 neighbourhoods' designs. Results show a high agreement level between experts on the performance level of the designs. The study contributes to computational sustainability by showing the potential in evaluation-automation of urban resiliency, ecological enhancement, and design for wellbeing, using expert knowledge and machine learning. |
| keywords |
Urban Design, Landscape Architecture, Computational Sustainability, Machine Learning, Convolutional Neural Network, Llandscape Sustainability, SDG 9, SDG 11, SDG 13, SDG 15 |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (953,857 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/07/22 07:34 |
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