TerraFrame proposes advancing the next generation of Spatial Data Infrastructures (SDIs) through GeoAI-enabled knowledge networks, building on the concept of a Spatial Knowledge Mesh (SKM). This approach moves beyond traditional centralized SDIs to decentralized, intelligent, and interoperable ecosystems, aligning with UN-GGIM recommendations and enabling Graph-augmented Retrieval-Augmented Generation (GraphRAG) for location-aware AI and decision-making. This means that if someone wants to build an LLM solution for disaster resilience, data on hydrography, population, and transportation can be discovered, integrated, and made instantly usable by AI. The aim is to make GeoAI solutions accessible and sustainable, even for lower-resourced governments and institutions.

Key Features

• Spatial Knowledge Mesh (SKM) Architecture
  • Decentralized data architecture inspired by Data Mesh, designed for geospatial interoperability. Exposes semantically rich, machine-readable spatial data that aligns automatically by geography and time. Supports federated GeoAI Knowledge Networks for real-time insights.

• GraphRAG-Enabled GeoAI Systems
  • On-demand creation of geospatial knowledge graphs to enhance Large Language Models (LLMs). Supports graph-augmented retrieval for context-aware, cross-domain AI decision-making.

• Integration Across Data Silos & Domains
  • Combines Earth observation data, sensor networks, and application-specific datasets. Leverages and modernizes existing SDI investments to create dynamic, knowledge-driven ecosystems.

• Open-Source and Standards-Aligned
  • Built on open-source principles with tools for semantic interoperability and automated knowledge alignment. Active contributor to OGC DWGs and SWGs for GeoAI, GeoSemantics, and Geo-Reporting.

• Collaborative Engagement
  • Ongoing projects with USACE, USDA, USDOI, and planned collaboration with a European SDI agency. Builds on real-world pilots such as OGC Climate and Disaster Resilience projects.

Benefits & Impact

• Modernizes Traditional SDIs:
  Evolves from static data repositories to dynamic knowledge ecosystems.
• Accelerates AI-Driven Insights:
  Supports location-aware AI and intelligent agents for decision-making.
• Semantic Interoperability:
  Automates alignment across heterogeneous data sources and standards.
• Scalable and Sustainable:
  Enables both high-resourced and lower-resourced organizations to leverage GeoAI.
• Supports Global Resilience Goals:
  Enhances capabilities for disaster response, public health, and economic planning.

Use Cases

• Disaster resilience and response
• Defense and national security
• Public health and environmental monitoring
• Economic development and infrastructure planning
• Next-generation NSDI hub contributions