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What are the current techniques used by AI to address scalability challenges in urban planning?

Hyperparameter optimization

Convolutional neural network

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Current techniques used by AI to address scalability challenges in urban planning include deep learning models like CNN and RNN, which have shown significant performance gains in various urban-computing tasks . Additionally, the development of Urban-GAN, a plural urban design computation system, leverages deep convolutional neural networks, case-based reasoning, and generative adversarial networks to empower the public in generating their own urban designs . Furthermore, CrowdOptim, a crowd-AI collaborative learning framework, integrates crowdsourcing, hyperparameter optimization, and estimation theory to optimize neural network hyperparameters in smart urban sensing applications, demonstrating superior performance compared to existing baselines . These AI techniques play a crucial role in overcoming scalability challenges and enhancing the efficiency of urban planning processes.

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Open access•Book Chapter•DOI AI and Deep Learning for Urban Computing Senzhang Wang, Jiannong Cao - Show less +1 more 07 Apr 2021 2 Citations	Current AI techniques like deep learning, CNN, RNN, and reinforcement learning are utilized for scalability challenges in urban planning, enhancing urban computing applications.
Journal Article•DOI Sequential Masterplanning: Using Urban‐GANs Wanyu He 07 Apr 2022-Architectural Design	Not addressed in the paper.
Book Chapter•DOI Classes of AI tools, techniques, and methods Sreenath V. Sharma 01 Jan 2022	The paper discusses AI tools, techniques, and methods in urban design. Techniques like intelligent agents can address scalability challenges in urban planning, adapting to practitioner objectives.
Journal Article•DOI CrowdOptim: A Crowd-driven Neural Network Hyperparameter Optimization Approach to AI-based Smart Urban Sensing Yan Zhang, Ruohan Zong, Lanyu Shang, Ziyi Kou, Huimin Zeng, Dong Wang - Show less +5 more 07 Nov 2022-Proceedings of the ACM on human-computer interaction 1 Citations	Not addressed in the paper.
Journal Article•DOI Urban-GAN: An artificial intelligence-aided computation system for plural urban design Steven Jige Quan 12 May 2022-Environment And Planning B: Urban Analytics And City Science 5 Citations	The paper introduces Urban-GAN, utilizing deep convolutional neural networks, case-based reasoning, and generative adversarial networks to empower the public in generating diverse urban designs, addressing scalability challenges in urban planning.

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