Big Data Fusion-Driven Geospatial Knowledge Graph Construction Method for Sustainable Smart Cities
Published in Sustainable Cities and Society, 2026
Urban planning faces increasing challenges in integrating and analyzing multi-source geospatial data due to inconsistencies in spatial resolution, data latency, and processing inefficiency. Traditional geographic information systems (GIS) and remote sensing models typically rely on a single data source, limiting their ability to deliver accurate and comprehensive insights for smart city development. This paper proposes a Big Data Fusion-Driven Geospatial Knowledge Graph framework (BDF-GeoKG) to address these limitations by integrating vector, raster, text, and image data. The proposed framework follows a structured process of entity extraction, relationship construction, attribute extraction, and entity alignment to establish a unified geospatial knowledge graph. Entity extraction identifies geographic objects and attributes from multi-source data. Relationship construction defines spatial and semantic connections …
Recommended citation: Y Duan, M Liang*, Y Li, R Gao, J Chen, ZS Chen, H Wang. (2026). Big Data Fusion-Driven Geospatial Knowledge Graph Construction Method for Sustainable Smart Cities. Sustainable Cities and Society. https://www.sciencedirect.com/science/article/pii/S2210670725008972