Morphological Regeneration of the Industrial Waterfront Based on Machine Learning

Shuyi Huang; Hao Zheng

doi:10.52842/conf.caadria.2022.1.475

Morphological Regeneration of the Industrial Waterfront Based on Machine Learning

Shuyi Huang
, Hao Zheng

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

3 Citations (Scopus)

Abstract

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. © 2022, The Association for Computer-Aided Architectural Design Research in Asia. All rights reserved.

Original language	English
Title of host publication	Proceedings of the 27th International Conference of the Association for Computer Aided Architectural Design Research in Asia (CAADRIA) 2022
Publisher	The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA)
Pages	475-484
Number of pages	10
Volume	1
ISBN (Print)	9789887891772
DOIs	https://doi.org/10.52842/conf.caadria.2022.1.475
Publication status	Published - 12 Apr 2022
Externally published	Yes
Event	27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA 2022) - Sydney, Australia Duration: 9 Apr 2022 → 15 Apr 2022 https://caadria2022.org/conference/

Publication series

Name	Proceedings of the International Conference on Computer-Aided Architectural Design Research in Asia
ISSN (Print)	2710-4257
ISSN (Electronic)	2710-4265

Conference

Conference	27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA 2022)
Place	Australia
City	Sydney
Period	9/04/22 → 15/04/22
Internet address	https://caadria2022.org/conference/

UN SDGs

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

SDG 9 Industry, Innovation, and Infrastructure
SDG 11 Sustainable Cities and Communities

Research Keywords

Machine Learning
Urban Morphology
Industrial Waterfront Regeneration
Sustainable Cities
SDG 11

Access Output

10.52842/conf.caadria.2022.1.475

https://papers.cumincad.org/data/works/att/caadria2022_140.pdf

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

Huang, S., & Zheng, H. (2022). Morphological Regeneration of the Industrial Waterfront Based on Machine Learning. In Proceedings of the 27th International Conference of the Association for Computer Aided Architectural Design Research in Asia (CAADRIA) 2022 (Vol. 1, pp. 475-484). (Proceedings of the International Conference on Computer-Aided Architectural Design Research in Asia). The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA). https://doi.org/10.52842/conf.caadria.2022.1.475

Huang, Shuyi ; Zheng, Hao. / Morphological Regeneration of the Industrial Waterfront Based on Machine Learning. Proceedings of the 27th International Conference of the Association for Computer Aided Architectural Design Research in Asia (CAADRIA) 2022. Vol. 1 The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), 2022. pp. 475-484 (Proceedings of the International Conference on Computer-Aided Architectural Design Research in Asia).

@inproceedings{0da4769c306a448a8c27762988a064bd,

title = "Morphological Regeneration of the Industrial Waterfront Based on Machine Learning",

abstract = "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. {\textcopyright} 2022, The Association for Computer-Aided Architectural Design Research in Asia. All rights reserved.",

keywords = "Machine Learning, Urban Morphology, Industrial Waterfront Regeneration, Sustainable Cities, SDG 11",

author = "Shuyi Huang and Hao Zheng",

year = "2022",

month = apr,

day = "12",

doi = "10.52842/conf.caadria.2022.1.475",

language = "English",

isbn = "9789887891772",

volume = "1",

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 (CAADRIA)",

pages = "475--484",

booktitle = "Proceedings of the 27th International Conference of the Association for Computer Aided Architectural Design Research in Asia (CAADRIA) 2022",

note = "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",

url = "https://caadria2022.org/conference/",

}

Huang, S & Zheng, H 2022, Morphological Regeneration of the Industrial Waterfront Based on Machine Learning. in Proceedings of the 27th International Conference of the Association for Computer Aided Architectural Design Research in Asia (CAADRIA) 2022. vol. 1, Proceedings of the International Conference on Computer-Aided Architectural Design Research in Asia, The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), pp. 475-484, 27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA 2022), Sydney, Australia, 9/04/22. https://doi.org/10.52842/conf.caadria.2022.1.475

Morphological Regeneration of the Industrial Waterfront Based on Machine Learning. / Huang, Shuyi; Zheng, Hao.
Proceedings of the 27th International Conference of the Association for Computer Aided Architectural Design Research in Asia (CAADRIA) 2022. Vol. 1 The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), 2022. p. 475-484 (Proceedings of the International Conference on Computer-Aided Architectural Design Research in Asia).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Morphological Regeneration of the Industrial Waterfront Based on Machine Learning

AU - Huang, Shuyi

AU - Zheng, Hao

PY - 2022/4/12

Y1 - 2022/4/12

N2 - 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. © 2022, The Association for Computer-Aided Architectural Design Research in Asia. All rights reserved.

AB - 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. © 2022, The Association for Computer-Aided Architectural Design Research in Asia. All rights reserved.

KW - Machine Learning

KW - Urban Morphology

KW - Industrial Waterfront Regeneration

KW - Sustainable Cities

KW - SDG 11

UR - https://www.scopus.com/pages/publications/85189169311

UR - https://www.scopus.com/pages/publications/85189169311#tab=impact

U2 - 10.52842/conf.caadria.2022.1.475

DO - 10.52842/conf.caadria.2022.1.475

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9789887891772

VL - 1

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

SP - 475

EP - 484

BT - Proceedings of the 27th International Conference of the Association for Computer Aided Architectural Design Research in Asia (CAADRIA) 2022

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

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

Y2 - 9 April 2022 through 15 April 2022

ER -

Huang S, Zheng H. Morphological Regeneration of the Industrial Waterfront Based on Machine Learning. In Proceedings of the 27th International Conference of the Association for Computer Aided Architectural Design Research in Asia (CAADRIA) 2022. Vol. 1. The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA). 2022. p. 475-484. (Proceedings of the International Conference on Computer-Aided Architectural Design Research in Asia). doi: 10.52842/conf.caadria.2022.1.475

Morphological Regeneration of the Industrial Waterfront Based on Machine Learning

Abstract

Publication series

Conference

UN SDGs

Research Keywords

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this