Integrating Spatial Knowledge Graphs and Artificial Intelligence for Enhanced Urban Intelligence and Smart City Analytics

Abstract

The rapid urbanization of modern cities has intensified challenges related to environmental management, infrastructure optimization, and data-driven decision-making. This study presents an integrated framework that combines Spatial Knowledge Graphs (SKGs) with Artificial Intelligence (AI) and remote sensing technologies to enhance urban intelligence and smart city analytics. Using authentic datasets derived from Sentinel-2 imagery, OpenStreetMap, and real-time IoT sensor data, the research models spatiotemporal relationships among critical urban parameters such as land use, air quality, and traffic density. The developed SKG framework enables dynamic querying, semantic reasoning, and predictive modeling through Graph Neural Networks (GNNs) and spatial embeddings. Quantitative analyses reveal strong correlations between urban density, transportation networks, and pollution intensity, demonstrating superior predictive accuracy over traditional GIS-based approaches. The findings confirm that the integration of SKGs and AI supports advanced spatial reasoning, enabling adaptive and sustainable urban planning. This study contributes to the evolving discourse on smart city intelligence by establishing a scalable, data-driven framework for urban analytics, with implications for climate resilience, infrastructure management, and real-time decision support systems.