Summary of Leveraging Generative Ai For Urban Digital Twins: a Scoping Review on the Autonomous Generation Of Urban Data, Scenarios, Designs, and 3d City Models For Smart City Advancement, by Haowen Xu et al.
Leveraging Generative AI for Urban Digital Twins: A Scoping Review on the Autonomous Generation of Urban Data, Scenarios, Designs, and 3D City Models for Smart City Advancement
by Haowen Xu, Femi Omitaomu, Soheil Sabri, Sisi Zlatanova, Xiao Li, Yongze Song
First submitted to arxiv on: 29 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper surveys the integration of Generative Artificial Intelligence (AI) techniques with urban digital twins to address challenges in smart city applications. The study explores how generative AI models can be applied to various urban sectors, such as transportation, energy systems, building management, and urban design. The authors discuss the use of generative AI for data augmentation, synthetic data generation, automated 3D city modeling, and generative urban design optimization. This innovative integration has potential opportunities for more reliable, scalable, and automated smart city management. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make cities smarter using a new type of artificial intelligence called Generative AI. The researchers explore ways to combine this AI with digital models of cities to make urban management more efficient and sustainable. They discuss how generative AI can help with data quality, scenario generation, and designing better cities. This integration has the potential to revolutionize city management by making it more reliable, scalable, and automated. |
Keywords
» Artificial intelligence » Data augmentation » Optimization » Synthetic data
