UrbanGenoGAN: pioneering urban spatial planning using the synergistic integration of GAN, GA, and GIS

Author:

Cheng Wanyue,Chu Yusu,Xia Chenyuan,Zhang Boliang,Chen Junming,Jia Mengyan,Wang Wenxiao

Abstract

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.

Publisher

Frontiers Media SA

Subject

General Environmental Science

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Towards Responsible Urban Geospatial AI: Insights From the White and Grey Literatures;Journal of Geovisualization and Spatial Analysis;2024-06-26

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