Generative AI Meets Future Cities: Towards an Era of Autonomous Urban Intelligence
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866911212384550912 |
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| author | Wang, Dongjie Lu, Chang-Tien Ye, Xinyue Yigitcanlar, Tan Fu, Yanjie |
| author_facet | Wang, Dongjie Lu, Chang-Tien Ye, Xinyue Yigitcanlar, Tan Fu, Yanjie |
| contents | The two fields of urban planning and artificial intelligence (AI) arose and developed separately. However, there is now cross-pollination and increasing interest in both fields to benefit from the advances of the other. In the present paper, we introduce the importance of urban planning from the sustainability, living, economic, disaster, and environmental perspectives. We review the fundamental concepts of urban planning and relate these concepts to crucial open problems of machine learning, including adversarial learning, generative neural networks, deep encoder-decoder networks, conversational AI, and geospatial and temporal machine learning, thereby assaying how AI can contribute to modern urban planning. Thus, a central problem is automated land-use configuration, which is formulated as the generation of land uses and building configuration for a target area from surrounding geospatial, human mobility, social media, environment, and economic activities. Finally, we delineate some implications of AI for urban planning and propose key research areas at the intersection of both topics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_03892 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Generative AI Meets Future Cities: Towards an Era of Autonomous Urban Intelligence Wang, Dongjie Lu, Chang-Tien Ye, Xinyue Yigitcanlar, Tan Fu, Yanjie Artificial Intelligence Computers and Society Machine Learning The two fields of urban planning and artificial intelligence (AI) arose and developed separately. However, there is now cross-pollination and increasing interest in both fields to benefit from the advances of the other. In the present paper, we introduce the importance of urban planning from the sustainability, living, economic, disaster, and environmental perspectives. We review the fundamental concepts of urban planning and relate these concepts to crucial open problems of machine learning, including adversarial learning, generative neural networks, deep encoder-decoder networks, conversational AI, and geospatial and temporal machine learning, thereby assaying how AI can contribute to modern urban planning. Thus, a central problem is automated land-use configuration, which is formulated as the generation of land uses and building configuration for a target area from surrounding geospatial, human mobility, social media, environment, and economic activities. Finally, we delineate some implications of AI for urban planning and propose key research areas at the intersection of both topics. |
| title | Generative AI Meets Future Cities: Towards an Era of Autonomous Urban Intelligence |
| topic | Artificial Intelligence Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2304.03892 |