| id |
caadria2023_303 |
| authors |
Hu, Anqi, Yabuki, Nobuyoshi and Fukuda, Tomohiro |
| year |
2023 |
| title |
Development of a Method for Assessing the View Index of Plants of Interest Using Deep Learning |
| source |
Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 585–594 |
| doi |
https://doi.org/10.52842/conf.caadria.2023.1.585
|
| summary |
Urban planning often overlooks the diversity of plant species and the perspectives of pedestrians. This study introduces the View Index of Plants of Interest (VIPI) as a new index for evaluating street plants from a pedestrian perspective. VIPI uses image classification and semantic segmentation techniques and was applied to four popular ornamental street plants: cherry trees, maple trees, magnolia trees, and ginkgo trees. The model used achieved a high level of accuracy with a mean intersection over union (mIoU) of 81.06. And the VIPI satisfaction criteria were used to evaluate several cases. The results provide valuable insights for urban planners and policymakers, allowing for a more detailed and accurate evaluation of urban plants from a pedestrian perspective and can guide urban greening actions. Additionally, this study demonstrates the potential of utilizing computer science techniques to inform urban planning and design decisions. |
| keywords |
Deep learning, Image classification, Semantic segmentation, View Index of Plants of Interest (VIPI), Street view images, Urban green space |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (1,324,416 bytes) |
| references |
Content-type: text/plain
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| last changed |
2023/06/15 23:14 |
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