@inproceedings{f7bfa665fdd248d8879b29848c5a0515,
title = "MACHINE LEARNING TOOL FOR SUSTAINABILITY EVALUATION: THE CASE OF NEIGHBOURHOODS' DESIGN",
abstract = "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 = "Convolutional Neural Network, Llandscape Sustainability, Machine Learning, SDG 11, SDG 13, SDG 15, SDG 9, Urban Design, Landscape Architecture, Computational Sustainability",
author = "Raanan, {Noam M.} and Hatzav Yoffe and Gal Zeev and Grobman, {Yasha J.}",
note = "Publisher Copyright: {\textcopyright} 2022, The Association for Computer-Aided Architectural Design Research in Asia. All rights reserved.; 27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia, CAADRIA 2022 ; Conference date: 09-04-2022 Through 15-04-2022",
year = "2022",
doi = "10.52842/conf.caadria.2022.1.283",
language = "אנגלית",
isbn = "9789887891772",
series = "Proceedings of the International Conference on Computer-Aided Architectural Design Research in Asia",
pages = "283--291",
editor = "{van Ameijde}, Jeroen and Nicole Gardner and Hyun, {Kyung Hoon} and Dan Luo and Urvi Sheth",
booktitle = "POST-CARBON, Proceedings of the 27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia, CAADRIA 2022, Volume 1",
}