The pursuit of human-level artificial intelligence necessitates a rigorous examination of the spatial dimension of cognition. Artificial general intelligence systems are expected to operate across a vast array of domains with human-like adaptability. However, most existing AI systems remain disconnected from physical reality. This disconnect stems from a lack of structured understanding of geography, topology, and the temporal evolution of the built and natural environment. Therefore, the central hypothesis of this article is that geospatial knowledge, if structured appropriately, forms a foundational component of cognitive reasoning in artificial systems. This hypothesis motivates the development and integration of geospatial knowledge graphs, which represent real-world entities and their spatial relationships in a formal and queryable structure.
Spatial reasoning is indispensable to general intelligence. Human cognition is inherently spatial. It operates not only on abstract concepts but also on concrete relationships among locations, objects, and events situated in time and space. Humans effortlessly recognize the significance of distance, proximity, containment, adjacency, and orientation in problem-solving and decision-making. Consequently, any system aspiring to match human reasoning must be capable of perceiving, encoding, and manipulating spatial relationships. Artificial intelligence without spatial awareness can only operate within constrained digital environments. It cannot reason about infrastructure, environmental change, or urban dynamics without grounding its logic in a geospatial context. Thus, spatial cognition is not a peripheral feature but a core faculty of general intelligence.
We need to address the inadequacy of conventional geospatial data representations for intelligent reasoning. Raster and vector data structures encode geometries and attributes, but they lack semantic richness and relational depth. Geospatial knowledge graphs fill this void by providing formal semantics to spatial entities and their interconnections. These graphs represent entities such as cities, rivers, roads, and administrative units as nodes. Edges in the graph define topological or conceptual relationships. The resulting structure is amenable to logic-based inference, pattern recognition, and multi-hop queries. For example, a knowledge graph can model containment hierarchies such as a neighborhood within a city or track temporal changes such as the construction history of infrastructure. By explicitly encoding semantics and time, geospatial knowledge graphs enable a transition from map-based perception to knowledge-based reasoning.
We examine the emerging role of the Overture Maps Foundation in creating an open, standardized, and high-quality geospatial knowledge base. Founded by leading technology companies, the foundation provides a curated map of the physical world that includes building footprints, road networks, points of interest, and administrative boundaries. Unlike traditional maps, this dataset is versioned, semantically attributed, and designed for machine consumption. This makes it suitable for use in reasoning systems, digital twins, and autonomous agents. By standardizing the structure and schema of spatial entities, Overture facilitates interoperability among geospatial applications. Its role is akin to a spatial operating system upon which intelligent agents can rely for consistent context and reference. In this respect, Overture supports not only geospatial intelligence applications but also broader efforts in the development of grounded artificial intelligence.
We explore the federated integration of Wikidata and OpenStreetMap as a foundational layer for spatially enriched knowledge graphs. Wikidata is a structured knowledge graph containing millions of real-world concepts, many of which are geospatially referenced. OpenStreetMap is a community-driven map platform that encodes detailed geometries of physical features. The linking of these two resources via unique identifiers allows agents to associate spatial geometries with abstract concepts and multilingual labels. This enables semantic search and reasoning across domains such as culture, environment, and infrastructure. An agent can, for instance, identify all UNESCO heritage sites within a floodplain by querying the graph. This fusion of symbolic knowledge and spatial geometry is essential for creating agents that understand the world not merely as shapes and coordinates, but as places with meaning, history, and function.
We propose a layered architecture for integrating geospatial knowledge into AI systems. The foundational layer consists of standardized datasets such as those from Overture, OpenStreetMap, and Wikidata. Above this, semantic reasoning engines interpret the relationships among spatial entities using formal ontologies. The next layer incorporates temporal dynamics, allowing agents to reason about change, trends, and event sequences. A symbolic-numeric fusion layer integrates perceptual data from imagery or sensors with symbolic representations from the knowledge graph. Finally, the agent layer performs decision-making, planning, and adaptation. This architecture enables explainable, adaptive, and transferable spatial reasoning capabilities in AI agents. It ensures that knowledge is not static but evolves with real-world changes, supporting applications ranging from autonomous navigation to environmental monitoring and policy planning.
The final reflection emphasizes that geospatial intelligence is not merely a tool for specific domains such as urban planning or disaster management. Rather, it is a structural necessity for any artificial system that seeks to act coherently in the physical world. Knowledge must be grounded in place, time, and context. Spatial semantics provide the structure through which knowledge can be localized, queried, and applied. Geospatial knowledge graphs, when combined with open data initiatives and formal ontologies, offer a practical path toward such grounding. They transform static maps into dynamic reasoning substrates. As AI evolves toward generality, it must embrace the structured geography of human reasoning. This is not a supplement to intelligence. It is its spatial spine.