| id |
caadria2022_140 |
| authors |
Huang, Shuyi and Zheng, Hao |
| year |
2022 |
| title |
Morphological Regeneration of the Industrial Waterfront Based on Machine Learning |
| doi |
https://doi.org/10.52842/conf.caadria.2022.1.475
|
| 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. 475-484 |
| summary |
The regeneration of the industrial waterfront is a global issue, and its significance lies in transforming the waterfront brownfield into an eco-friendly, hospitable, and vibrant urban space. However, the industrial waterfront naturally has comparatively unmanageable morphological features, including linear shape, irregular waterfront boundary, and separation with urban networks. Therefore, how to subdivide the vacant land and determine the land-use type for each subdivision becomes a challenging problem. Accordingly, this study proposes an application of machine learning models. It allows the generation of morphological elements of the vacant industrial waterfront by comparing the before-and-after scenarios of successful regeneration projects. The data collected from New York City is used as a showcase of this method. |
| keywords |
machine learning, urban morphology, industrial waterfront regeneration, sustainable cities, SDG 11 |
| series |
CAADRIA |
| email |
zhhao@design.upenn.edu |
| full text |
 file.pdf (2,424,145 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/07/22 07:34 |
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