MACHINE LEARNING TOOL FOR SUSTAINABILITY EVALUATION: THE CASE OF NEIGHBOURHOODS' DESIGN

Noam M. Raanan; Hatzav Yoffe; Gal Zeev; Yasha J. Grobman

doi:10.52842/conf.caadria.2022.1.283

MACHINE LEARNING TOOL FOR SUSTAINABILITY EVALUATION: THE CASE OF NEIGHBOURHOODS' DESIGN

Noam M. Raanan
, Hatzav Yoffe
, Gal Zeev
, Yasha J. Grobman

Architecture and Town Planning

Technion - Israel Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English GB
Title of host publication	POST-CARBON, Proceedings of the 27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia, CAADRIA 2022, Volume 1
Editors	Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth
Publisher	The Association for Computer-Aided Architectural Design Research in Asia
Pages	283-291
Number of pages	9
ISBN (Print)	9789887891772
DOIs	https://doi.org/10.52842/conf.caadria.2022.1.283
State	Published - 2022
Event	27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia, CAADRIA 2022 - Virtual, Online Duration: 9 Apr 2022 → 15 Apr 2022

Publication series

Name	Proceedings of the International Conference on Computer-Aided Architectural Design Research in Asia

Conference

Conference	27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia, CAADRIA 2022
City	Virtual, Online
Period	9/04/22 → 15/04/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

Convolutional Neural Network
Llandscape Sustainability
Machine Learning
SDG 11
SDG 13
SDG 15
SDG 9
Urban Design, Landscape Architecture, Computational Sustainability

ASJC Scopus subject areas

Architecture
Building and Construction
Computer Graphics and Computer-Aided Design
Materials Science (miscellaneous)
Modelling and Simulation

Access to Document

10.52842/conf.caadria.2022.1.283

Cite this

Raanan, N. M., Yoffe, H., Zeev, G., & Grobman, Y. J. (2022). MACHINE LEARNING TOOL FOR SUSTAINABILITY EVALUATION: THE CASE OF NEIGHBOURHOODS' DESIGN. In J. van Ameijde, N. Gardner, K. H. Hyun, D. Luo, & U. Sheth (Eds.), POST-CARBON, Proceedings of the 27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia, CAADRIA 2022, Volume 1 (pp. 283-291). (Proceedings of the International Conference on Computer-Aided Architectural Design Research in Asia). The Association for Computer-Aided Architectural Design Research in Asia. https://doi.org/10.52842/conf.caadria.2022.1.283

Raanan, Noam M. ; Yoffe, Hatzav ; Zeev, Gal et al. / MACHINE LEARNING TOOL FOR SUSTAINABILITY EVALUATION : THE CASE OF NEIGHBOURHOODS' DESIGN. POST-CARBON, Proceedings of the 27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia, CAADRIA 2022, Volume 1. editor / Jeroen van Ameijde ; Nicole Gardner ; Kyung Hoon Hyun ; Dan Luo ; Urvi Sheth. The Association for Computer-Aided Architectural Design Research in Asia, 2022. pp. 283-291 (Proceedings of the International Conference on Computer-Aided Architectural Design Research in Asia).

@inproceedings{3f6fa9bb59ad406184b0e2ac9718b697,

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 = "الإنجليزيّة",

isbn = "9789887891772",

series = "Proceedings of the International Conference on Computer-Aided Architectural Design Research in Asia",

publisher = "The Association for 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",

}

Raanan, NM, Yoffe, H, Zeev, G & Grobman, YJ 2022, MACHINE LEARNING TOOL FOR SUSTAINABILITY EVALUATION: THE CASE OF NEIGHBOURHOODS' DESIGN. in J van Ameijde, N Gardner, KH Hyun, D Luo & U Sheth (eds), POST-CARBON, Proceedings of the 27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia, CAADRIA 2022, Volume 1. Proceedings of the International Conference on Computer-Aided Architectural Design Research in Asia, The Association for Computer-Aided Architectural Design Research in Asia, pp. 283-291, 27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia, CAADRIA 2022, Virtual, Online, 9/04/22. https://doi.org/10.52842/conf.caadria.2022.1.283

MACHINE LEARNING TOOL FOR SUSTAINABILITY EVALUATION: THE CASE OF NEIGHBOURHOODS' DESIGN. / Raanan, Noam M.; Yoffe, Hatzav; Zeev, Gal et al.
POST-CARBON, Proceedings of the 27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia, CAADRIA 2022, Volume 1. ed. / Jeroen van Ameijde; Nicole Gardner; Kyung Hoon Hyun; Dan Luo; Urvi Sheth. The Association for Computer-Aided Architectural Design Research in Asia, 2022. p. 283-291 (Proceedings of the International Conference on Computer-Aided Architectural Design Research in Asia).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - MACHINE LEARNING TOOL FOR SUSTAINABILITY EVALUATION

T2 - 27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia, CAADRIA 2022

AU - Raanan, Noam M.

AU - Yoffe, Hatzav

AU - Zeev, Gal

AU - Grobman, Yasha J.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Convolutional Neural Network

KW - Llandscape Sustainability

KW - Machine Learning

KW - SDG 11

KW - SDG 13

KW - SDG 15

KW - SDG 9

KW - Urban Design, Landscape Architecture, Computational Sustainability

UR - https://www.scopus.com/pages/publications/85174183835

U2 - 10.52842/conf.caadria.2022.1.283

DO - 10.52842/conf.caadria.2022.1.283

M3 - منشور من مؤتمر

SN - 9789887891772

T3 - Proceedings of the International Conference on Computer-Aided Architectural Design Research in Asia

SP - 283

EP - 291

BT - POST-CARBON, Proceedings of the 27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia, CAADRIA 2022, Volume 1

A2 - van Ameijde, Jeroen

A2 - Gardner, Nicole

A2 - Hyun, Kyung Hoon

A2 - Luo, Dan

A2 - Sheth, Urvi

PB - The Association for Computer-Aided Architectural Design Research in Asia

Y2 - 9 April 2022 through 15 April 2022

ER -

Raanan NM, Yoffe H, Zeev G, Grobman YJ. MACHINE LEARNING TOOL FOR SUSTAINABILITY EVALUATION: THE CASE OF NEIGHBOURHOODS' DESIGN. In van Ameijde J, Gardner N, Hyun KH, Luo D, Sheth U, editors, POST-CARBON, Proceedings of the 27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia, CAADRIA 2022, Volume 1. The Association for Computer-Aided Architectural Design Research in Asia. 2022. p. 283-291. (Proceedings of the International Conference on Computer-Aided Architectural Design Research in Asia). doi: 10.52842/conf.caadria.2022.1.283

MACHINE LEARNING TOOL FOR SUSTAINABILITY EVALUATION: THE CASE OF NEIGHBOURHOODS' DESIGN

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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