UrbanGenoGAN: pioneering urban spatial planning using the synergistic integration of GAN, GA, and GIS
Introduction: Urban spatial planning is critical for the development of sustainable and livable cities. However, traditional planning methods often face challenges in handling complex planning scenarios and large-scale data.Methods: This paper introduces UrbanGenoGAN, a novel algorithm that integrat...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-12-01
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| Series: | Frontiers in Environmental Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenvs.2023.1287858/full |
| _version_ | 1797397177708838912 |
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| author | Wanyue Cheng Yusu Chu Chenyuan Xia Boliang Zhang Junming Chen Mengyan Jia Wenxiao Wang |
| author_facet | Wanyue Cheng Yusu Chu Chenyuan Xia Boliang Zhang Junming Chen Mengyan Jia Wenxiao Wang |
| author_sort | Wanyue Cheng |
| collection | DOAJ |
| description | Introduction: Urban spatial planning is critical for the development of sustainable and livable cities. However, traditional planning methods often face challenges in handling complex planning scenarios and large-scale data.Methods: This paper introduces UrbanGenoGAN, a novel algorithm that integrates generative adversarial networks (GANs), genetic optimization algorithms (GOAs), and geographic information system (GIS) to address these challenges. Leveraging the generative power of GANs, the optimization capabilities of genetic algorithms, and the spatial analysis capabilities of GIS, UrbanGenoGAN is designed to generate optimized urban plans that cater to various urban planning challenges. Our methodology details the algorithm’s design and integration of its components, data collection and preprocessing, and the training and implementation processes.Results: Through rigorous evaluation metrics, comparative analysis with existing methodologies, and case studies, the proposed algorithm demonstrates significant improvement in urban planning outcomes. The research also explores the technical and practical considerations for implementing UrbanGenoGAN, including scalability, computational efficiency, data privacy, and ethical considerations.Discussion: The findings suggest that the integration of advanced machine learning and optimization techniques with spatial analysis offers a promising approach to enhancing decision-making in urban spatial planning. This work contributes to the growing field of AI applications in urban planning and paves the way for more efficient and sustainable urban development. |
| first_indexed | 2024-03-09T01:06:12Z |
| format | Article |
| id | doaj.art-f3022eb34c3f45fc825ccef29a14b5ae |
| institution | Directory Open Access Journal |
| issn | 2296-665X |
| language | English |
| last_indexed | 2024-03-09T01:06:12Z |
| publishDate | 2023-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Environmental Science |
| spelling | doaj.art-f3022eb34c3f45fc825ccef29a14b5ae2023-12-11T09:37:26ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2023-12-011110.3389/fenvs.2023.12878581287858UrbanGenoGAN: pioneering urban spatial planning using the synergistic integration of GAN, GA, and GISWanyue Cheng0Yusu Chu1Chenyuan Xia2Boliang Zhang3Junming Chen4Mengyan Jia5Wenxiao Wang6Faculty of Humanities and Arts, Macau University of Science and Technology, Macau, Macau SAR, ChinaDepartment of Urban Studies and Planning, The University of Sheffield, Sheffield, United KingdomFaculty of Humanities and Arts, Macau University of Science and Technology, Macau, Macau SAR, ChinaFaculty of Applied Sciences, Macao Polytechnic University, Macao, ChinaFaculty of Humanities and Arts, Macau University of Science and Technology, Macau, Macau SAR, ChinaJiyang College of Zhejiang A&F University, Zhuji, ChinaFaculty of Humanities and Arts, Macau University of Science and Technology, Macau, Macau SAR, ChinaIntroduction: Urban spatial planning is critical for the development of sustainable and livable cities. However, traditional planning methods often face challenges in handling complex planning scenarios and large-scale data.Methods: This paper introduces UrbanGenoGAN, a novel algorithm that integrates generative adversarial networks (GANs), genetic optimization algorithms (GOAs), and geographic information system (GIS) to address these challenges. Leveraging the generative power of GANs, the optimization capabilities of genetic algorithms, and the spatial analysis capabilities of GIS, UrbanGenoGAN is designed to generate optimized urban plans that cater to various urban planning challenges. Our methodology details the algorithm’s design and integration of its components, data collection and preprocessing, and the training and implementation processes.Results: Through rigorous evaluation metrics, comparative analysis with existing methodologies, and case studies, the proposed algorithm demonstrates significant improvement in urban planning outcomes. The research also explores the technical and practical considerations for implementing UrbanGenoGAN, including scalability, computational efficiency, data privacy, and ethical considerations.Discussion: The findings suggest that the integration of advanced machine learning and optimization techniques with spatial analysis offers a promising approach to enhancing decision-making in urban spatial planning. This work contributes to the growing field of AI applications in urban planning and paves the way for more efficient and sustainable urban development.https://www.frontiersin.org/articles/10.3389/fenvs.2023.1287858/fulldata optimizationmachine learningsustainable urban developmentUrbanGenoGANurban spatial planning |
| spellingShingle | Wanyue Cheng Yusu Chu Chenyuan Xia Boliang Zhang Junming Chen Mengyan Jia Wenxiao Wang UrbanGenoGAN: pioneering urban spatial planning using the synergistic integration of GAN, GA, and GIS Frontiers in Environmental Science data optimization machine learning sustainable urban development UrbanGenoGAN urban spatial planning |
| title | UrbanGenoGAN: pioneering urban spatial planning using the synergistic integration of GAN, GA, and GIS |
| title_full | UrbanGenoGAN: pioneering urban spatial planning using the synergistic integration of GAN, GA, and GIS |
| title_fullStr | UrbanGenoGAN: pioneering urban spatial planning using the synergistic integration of GAN, GA, and GIS |
| title_full_unstemmed | UrbanGenoGAN: pioneering urban spatial planning using the synergistic integration of GAN, GA, and GIS |
| title_short | UrbanGenoGAN: pioneering urban spatial planning using the synergistic integration of GAN, GA, and GIS |
| title_sort | urbangenogan pioneering urban spatial planning using the synergistic integration of gan ga and gis |
| topic | data optimization machine learning sustainable urban development UrbanGenoGAN urban spatial planning |
| url | https://www.frontiersin.org/articles/10.3389/fenvs.2023.1287858/full |
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