Spatiotemporal Graph Learning on Urban Environments

Urban environments are shaped by intricate interactions among water, soil, air, and infrastructure, where traditional models often fail to capture nonlinear, non-Euclidean dynamics. Spatiotemporal graph learning (STGL) has emerged as a powerful framework to represent such complexity, enabling accurate forecasting and real-time decision support from urban districts to national and even global scales. This review provides the first comprehensive synthesis of STGL tailored to urban environments. We summarize advances in graph construction, spatial and temporal modeling, and fusion strategies, and examine applications across urban water systems, soil and agriculture, air quality, and urban risk. Landmark case studies, including Microsoft's Aurora, NVIDIA's Earth-2, and Google's GraphCast/GenCast, demonstrate STGL's potential as a foundation model for environmental intelligence. We conclude by identifying key limitations and outlining future directions, emphasizing federated learning, machine unlearning, and meta-learning to enhance next-generation STGL frameworks that ultimately support resilient and adaptive urban environments.