Identifying industrial buildings as a spatial resource for sustainable urban regeneration in high-density post-industrial metropolitan in Asia
Industrial buildings are an important spatial resource and play a crucial role in sustainable urban regeneration of high-density post-industrial metropolitan in Asia with insufficient spatial resources. This paper develops a machine learning method for identifying industrial buildings from satellite...
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Format: | Article |
Language: | English |
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Taylor & Francis Group
2025-01-01
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Series: | Journal of Asian Architecture and Building Engineering |
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Online Access: | http://dx.doi.org/10.1080/13467581.2025.2455026 |
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author | Miao Sun Tan Qin Yuanxiao Kuang Jianchang Lv |
author_facet | Miao Sun Tan Qin Yuanxiao Kuang Jianchang Lv |
author_sort | Miao Sun |
collection | DOAJ |
description | Industrial buildings are an important spatial resource and play a crucial role in sustainable urban regeneration of high-density post-industrial metropolitan in Asia with insufficient spatial resources. This paper develops a machine learning method for identifying industrial buildings from satellite aerial images. It extracts vector footprints of buildings from aerial imagery through image segmentation, establishes a feature engineering model comprising 11 distinct indicators, and introduces a Random Forest model to enhance the analysis. By mining the implicit spatial design requirements present in geographical information, this methodology facilitates the classification of industrial buildings from hundreds of thousands of buildings. The results demonstrate that the identification of typical characteristics can discover the scale, distribution, and surrounding built environment of industrial buildings in Shanghai’s Central City, providing valuable data for managing industrial spatial resources from lot to building granularity, implementing a systematical and comprehensive re-planning, and popularizing adaptive reuse strategy, with the goal of leading a shift in policies and paradigms of urban regeneration for improvement of efficiency, balance, and green transformation in high-density post-industrial metropolitan in Asia. |
format | Article |
id | doaj-art-5d4eb91fffbf456d937b806ad95990d1 |
institution | Kabale University |
issn | 1347-2852 |
language | English |
publishDate | 2025-01-01 |
publisher | Taylor & Francis Group |
record_format | Article |
series | Journal of Asian Architecture and Building Engineering |
spelling | doaj-art-5d4eb91fffbf456d937b806ad95990d12025-02-05T12:46:13ZengTaylor & Francis GroupJournal of Asian Architecture and Building Engineering1347-28522025-01-010011810.1080/13467581.2025.24550262455026Identifying industrial buildings as a spatial resource for sustainable urban regeneration in high-density post-industrial metropolitan in AsiaMiao Sun0Tan Qin1Yuanxiao Kuang2Jianchang Lv3Shanghai UniversityShanghai UniversityShanghai Metro Data Technology Co. LtdShanghai UniversityIndustrial buildings are an important spatial resource and play a crucial role in sustainable urban regeneration of high-density post-industrial metropolitan in Asia with insufficient spatial resources. This paper develops a machine learning method for identifying industrial buildings from satellite aerial images. It extracts vector footprints of buildings from aerial imagery through image segmentation, establishes a feature engineering model comprising 11 distinct indicators, and introduces a Random Forest model to enhance the analysis. By mining the implicit spatial design requirements present in geographical information, this methodology facilitates the classification of industrial buildings from hundreds of thousands of buildings. The results demonstrate that the identification of typical characteristics can discover the scale, distribution, and surrounding built environment of industrial buildings in Shanghai’s Central City, providing valuable data for managing industrial spatial resources from lot to building granularity, implementing a systematical and comprehensive re-planning, and popularizing adaptive reuse strategy, with the goal of leading a shift in policies and paradigms of urban regeneration for improvement of efficiency, balance, and green transformation in high-density post-industrial metropolitan in Asia.http://dx.doi.org/10.1080/13467581.2025.2455026sustainable urban regenerationindustrial building identificationspatial resourceadaptive reusepost-industrial metropolis in asia |
spellingShingle | Miao Sun Tan Qin Yuanxiao Kuang Jianchang Lv Identifying industrial buildings as a spatial resource for sustainable urban regeneration in high-density post-industrial metropolitan in Asia Journal of Asian Architecture and Building Engineering sustainable urban regeneration industrial building identification spatial resource adaptive reuse post-industrial metropolis in asia |
title | Identifying industrial buildings as a spatial resource for sustainable urban regeneration in high-density post-industrial metropolitan in Asia |
title_full | Identifying industrial buildings as a spatial resource for sustainable urban regeneration in high-density post-industrial metropolitan in Asia |
title_fullStr | Identifying industrial buildings as a spatial resource for sustainable urban regeneration in high-density post-industrial metropolitan in Asia |
title_full_unstemmed | Identifying industrial buildings as a spatial resource for sustainable urban regeneration in high-density post-industrial metropolitan in Asia |
title_short | Identifying industrial buildings as a spatial resource for sustainable urban regeneration in high-density post-industrial metropolitan in Asia |
title_sort | identifying industrial buildings as a spatial resource for sustainable urban regeneration in high density post industrial metropolitan in asia |
topic | sustainable urban regeneration industrial building identification spatial resource adaptive reuse post-industrial metropolis in asia |
url | http://dx.doi.org/10.1080/13467581.2025.2455026 |
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