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
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 | American English |
|---|---|
| Title of host publication | CAADRIA 2022 POST-CARBON International Conference for The Association for Computer-Aided Architectural Design Research in Asia |
| Pages | 283-291 |
| State | Published - 2022 |