UrbanGenoGAN: pioneering urban spatial planning using the synergistic integration of GAN, GA, and GIS - Cheng, Wanyue; Chu, Yusu; Xia, Chenyuan; Zhang, Boliang; Chen, Junming; Jia, Mengyan; Wang, Wenxiao (Frontiers in Environmental Science, 2023)
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. This paper introduces UrbanGenoGAN, a novel algorithm that integrates generative adversarial networks (GANs), genetic optimization algorithms (GOAs), and geographic information systems (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. 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. 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.