Artificial intelligence in urban science: why does it matter? - Xinyue Ye; Tan Yigitcanlar; Michael Goodchild; Xiao Huang; Wenwen Li; Shih-Lung Shaw; Yanjie Fu; Wenjing Gong; Galen Newman (Annals of GIS)
Urban science aims to explain, discover, understand, and generalize (EDUG) complex, human-centric systems, emphasizing societal context and sustainability. However, integrating artificial intelligence (AI) into urban science presents challenges, including data availability, ethical considerations, and the ‘black-box’ nature of many AI models. Despite these limitations, AI offers significant opportunities for urban management and planning by leveraging vast, multimodal datasets to optimize infrastructure, predict trends, and enhance resilience. Techniques such as explainable AI and knowledge-driven approaches have begun addressing transparency concerns, aligning AI outputs with urban science’s emphasis on interpretability. Urban science reciprocally contributes to AI development by embedding contextual awareness and human-centric insights, enhancing AI’s ability to navigate urban complexities. Examples include digital twins for real-time urban analysis and generative AI for inclusive urban modelling. This opinion piece advocates for fostering a symbiotic relationship between AI and urban science, emphasizing co-learning and ethical collaboration. By integrating technical innovation with societal needs, the convergence of AI and urban science – termed the ‘New Urban Science’ – promises smarter, equitable, and sustainable cities. This paradigm underscores the transformative potential of aligning AI advancements with urban science’s foundational goals.