Enhancing Urban Space Optimization and Governance through Artificial Intelligence: Insights from Megacities
With rapid urbanization, megacities worldwide are transitioning to stock optimization and reduction planning to address spatial constraints. Dynamic monitoring and functional optimization of construction space have emerged as crucial areas of research for interdisciplinary studies on urban inventory...
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| Format: | Article |
| Language: | English |
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American Association for the Advancement of Science (AAAS)
2025-01-01
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| Series: | Journal of Remote Sensing |
| Online Access: | https://spj.science.org/doi/10.34133/remotesensing.0748 |
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| author | Tengyun Hu Tuo Chen Guojiang Yu Meng Zhang Yan Ding Han Liu Yu Wang |
| author_facet | Tengyun Hu Tuo Chen Guojiang Yu Meng Zhang Yan Ding Han Liu Yu Wang |
| author_sort | Tengyun Hu |
| collection | DOAJ |
| description | With rapid urbanization, megacities worldwide are transitioning to stock optimization and reduction planning to address spatial constraints. Dynamic monitoring and functional optimization of construction space have emerged as crucial areas of research for interdisciplinary studies on urban inventory and reduction planning. The advent of artificial intelligence technology and high-resolution remote sensing images enables building footprint extraction and urban function identification at a relatively large scale. However, there have been limited recent attempts to explore the reuse of buildings with altered functions. Here, we introduced an artificial intelligence technology framework for implementing urban inventory and reduction planning, using Beijing as an example. Utilizing the U-Net deep learning model and high-frequency remote sensing images, we analyzed building space dynamics in Beijing between 2018 and 2019. Then, by combining the random forest regression method with data on vacated building spaces, we developed strategies to optimize and simulate the layout for various reuse functions, including recultivation and regreening in the planned non-construction areas, as well as residential, industrial, and public service facilities in planned construction areas. Our results reveal a substantial decrease in building area, primarily affecting environmentally important areas such as agricultural land areas and urban green spaces. These findings align with the reduction requirements outlined in the latest version of the Beijing Urban Master Plan (2016–2035), in accordance with the updated General Regulations and the initiative “Relief, Remediation and Promotion”. Based on the implementation principles of inventory and reduction in the new General Plan, the study explored optimized layouts for future reuse functions within planned construction and non-construction spaces. This innovative approach to urban space optimization and governance simulation offers valuable scientific insights and decision-making support for urban structure adjustments and land use efficiency enhancements in megacities. |
| format | Article |
| id | doaj-art-337ee08fc4094fad894d147f6d188e2e |
| institution | DOAJ |
| issn | 2694-1589 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Journal of Remote Sensing |
| spelling | doaj-art-337ee08fc4094fad894d147f6d188e2e2025-08-20T02:46:39ZengAmerican Association for the Advancement of Science (AAAS)Journal of Remote Sensing2694-15892025-01-01510.34133/remotesensing.0748Enhancing Urban Space Optimization and Governance through Artificial Intelligence: Insights from MegacitiesTengyun Hu0Tuo Chen1Guojiang Yu2Meng Zhang3Yan Ding4Han Liu5Yu Wang6School of Architecture, Tsinghua University, Beijing 100084, China.College of Land Science and Technology, China Agricultural University, Beijing 100083, China.College of Land Science and Technology, China Agricultural University, Beijing 100083, China.Beijing City Interface Technology Co. Ltd., Beijing 100037, China.School of Geographic Sciences, East China Normal University, Shanghai 200241, China.Land Consolidation and Rehabilitation Center, Ministry of Natural Resources, Beijing 100035, China.National Disaster Reduction Center of China, Ministry of Emergency Management, Beijing 100124, China.With rapid urbanization, megacities worldwide are transitioning to stock optimization and reduction planning to address spatial constraints. Dynamic monitoring and functional optimization of construction space have emerged as crucial areas of research for interdisciplinary studies on urban inventory and reduction planning. The advent of artificial intelligence technology and high-resolution remote sensing images enables building footprint extraction and urban function identification at a relatively large scale. However, there have been limited recent attempts to explore the reuse of buildings with altered functions. Here, we introduced an artificial intelligence technology framework for implementing urban inventory and reduction planning, using Beijing as an example. Utilizing the U-Net deep learning model and high-frequency remote sensing images, we analyzed building space dynamics in Beijing between 2018 and 2019. Then, by combining the random forest regression method with data on vacated building spaces, we developed strategies to optimize and simulate the layout for various reuse functions, including recultivation and regreening in the planned non-construction areas, as well as residential, industrial, and public service facilities in planned construction areas. Our results reveal a substantial decrease in building area, primarily affecting environmentally important areas such as agricultural land areas and urban green spaces. These findings align with the reduction requirements outlined in the latest version of the Beijing Urban Master Plan (2016–2035), in accordance with the updated General Regulations and the initiative “Relief, Remediation and Promotion”. Based on the implementation principles of inventory and reduction in the new General Plan, the study explored optimized layouts for future reuse functions within planned construction and non-construction spaces. This innovative approach to urban space optimization and governance simulation offers valuable scientific insights and decision-making support for urban structure adjustments and land use efficiency enhancements in megacities.https://spj.science.org/doi/10.34133/remotesensing.0748 |
| spellingShingle | Tengyun Hu Tuo Chen Guojiang Yu Meng Zhang Yan Ding Han Liu Yu Wang Enhancing Urban Space Optimization and Governance through Artificial Intelligence: Insights from Megacities Journal of Remote Sensing |
| title | Enhancing Urban Space Optimization and Governance through Artificial Intelligence: Insights from Megacities |
| title_full | Enhancing Urban Space Optimization and Governance through Artificial Intelligence: Insights from Megacities |
| title_fullStr | Enhancing Urban Space Optimization and Governance through Artificial Intelligence: Insights from Megacities |
| title_full_unstemmed | Enhancing Urban Space Optimization and Governance through Artificial Intelligence: Insights from Megacities |
| title_short | Enhancing Urban Space Optimization and Governance through Artificial Intelligence: Insights from Megacities |
| title_sort | enhancing urban space optimization and governance through artificial intelligence insights from megacities |
| url | https://spj.science.org/doi/10.34133/remotesensing.0748 |
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