Geo-AI in spatial data infrastructure: a comprehensive review

Abstract

The integration of Geographic Artificial Intelligence (GeoAI) into Spatial Data Infrastructure (SDI) represents a transformative shift in geospatial science, offering advanced capabilities for data processing, analysis, and decisionmaking. This article explores the progress, challenges, and opportunities of GeoAI in enhancing SDI frameworks. GeoAI’s ability to automate complex spatial analyses, process large-scale datasets, and generate predictive models has improved the efficiency and accuracy of geospatial data management. Despite these advancements, challenges such as data heterogeneity, ethical considerations, and the digital divide persist. The article examines recent advancements in machine learning, computer vision, and natural language processing applied to SDI, emphasizing their role in fostering smarter, data-driven spatial solutions. It also discusses the potential of GeoAI to address critical issues in climate resilience, urban planning, and disaster management. By identifying key opportunities for innovation, this study underscores the need for interdisciplinary collaboration, standardized frameworks, and policy alignment to maximize the benefits of GeoAI in SDI. This synthesis aims to guide future research and practical implementation, paving the way for a more adaptive and intelligent spatial data ecosystem.