Organizing the World in Grid Cells: The AI-DGGS Pilot

Call For Participation

The OGC AI-DGGS for Disaster Management Pilot seeks to test, enable, and improve AI-augmented Discrete Global Grid Systems (DGGS). DGGS represent locations as cells, moving beyond traditional geographic reference systems. As a result, DGGS can provide a foundation for a global localization system in which every object in the real world has a unique location identifier. DGGS are well-suited for data integration and efficient querying and analysis. Therefore, they are ideally positioned to serve as authoritative ‘content stores’ for AI-powered natural language queries.

This interoperability initiative, known as the “AI-DGGS for Disaster Management Pilot,” seeks to explore and demonstrate how systems using different DGGS grid designs can use OGC Standards to automatically exchange data and information, with a particular view on supporting disaster mapping. Further, the Pilot will explore how DGGS systems can be enhanced with standards-based AI/ML tools—such as AI chatbots for natural language queries and AI-driven insights for automated analysis and pattern detection—using, for example, a Retrieval-Augmented Generation (RAG) approach that provides accurate, authoritative, hallucination-free responses.

On the data integration side, DGGS can serve as a localization identifier system for any real-world object. DGGS organizes space using nested cells. A list of cell IDs describes the exact geometries of real-world objects, where the accuracy depends only on the cell size. Since DGGS can map an index of all cell sizes, data described at different “zoom levels” can be efficiently integrated and linked. This makes DGGS an ideal localization & identification system for many domains, such as finance, disaster management, and environmental analysis. It can serve as a replacement or enhancement for postal addresses, as it eliminates the need for descriptive addresses or physical landmarks. Even areas without traditional infrastructure, such as areas without roads, can be precisely identified. Similarly, a DGGS never struggles to adapt to dynamic urban growth.

DGGS: A natively digital reference system 

For historical reasons, Geographic Information Systems (GIS) use a projected (and thus distorted), scale-dependent representation of Earth originally designed for navigation using paper maps. In contrast, Discrete Global Grid Systems (DGGS) offer a natively digital cell-based system that accurately represents Earth as a spheroid. DGGS partitions the planet into hierarchically tessellated cells that span all scales. This creates a “spreadsheet for Earth” that enables incredibly efficient analysis and integration of large datasets.

Using cells of fixed area, information recorded about phenomena at a location can be easily referenced to the explicit area of its associated cell(s), quickly integrated with other cell values from different datasets, and efficiently analyzed to provide valid summaries for any chosen selection of cells. DGGS offer a much simpler method to ingest statistical and other gridded information into broader geospatial systems, unlocking tremendous potential for improved contextual analysis and insight.

DGGS can be constructed in multiple ways to meet diverse goals. Achieving interoperability between different DGGS will be necessary to ensure individuals and software using differing DGGS approaches can work together seamlessly. The DGGS Pilot seeks to understand and demonstrate such interoperability.

Integration Across DGGS Systems

The Pilot will explore and demonstrate how geospatial data standards can enable DGGS interoperability in disaster management or other domains of interest to Pilot Sponsors. This will be achieved by prototyping different DGGS solutions that use geospatial data standards to work together to deliver geospatial integration, analysis, and visualization functionality to support disaster management scenarios. Outcomes will provide insight into the ability of current and emerging OGC Standards to meet these interoperability requirements. This will lead to future improvements in the design of OGC Standards, including the potential to create new Standards where needed.

Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is an approach to large language model (LLM) (aka natural language) powered AI ‘chatbots’ that adds an additional, authoritative content store to which the LLM can refer for its responses. Using this approach, LLM AI models become an interface for queries of authoritative sources rather than the sources themselves. The approach gives the benefits of natural language queries without the risk of hallucinations occurring from the extrapolation of the model’s training data.  

The AI-DGGS Pilot seeks to investigate how DGGS can be used as content stores in ‘AI chatbots’, enabling natural language queries such as “How many people live in this street that needs to be evacuated?” or “Identify which neighborhoods are likely to be impacted by flooding.”

Pilot Objectives

Through this project, we will understand the ability of OGC data discovery, access, and processing standards to enable independent DGGS to integrate or interface in various contexts. The project will explore AI technologies’ role in querying and visualization of DGGS in support of knowledge generation. This will help you us understand the current state of DGGS interoperability in the context of rapid data integration, determine the suitability of DGGS technology to meet disaster management or other requirements (e.g., data integration, analysis, and visualization as well as real-world object localization), and identify additional work that will be required to fully realize DGGS interoperability for disaster management, financial services, insurance services, and other applications. 

Detailed objectives are described through the following points:

1. Data Integration and Management:
   
   
   • Demonstrate how OGC standards enable integration of diverse forms of geospatial data (e.g. climate, weather, statistical) using DGGS.
     
     
   • Demonstrate the ability of OGC standards to enable the use of AI to support DGGS data integration and management.
     
     
2. Analysis and Visualization:
   
   
   • Prototype the use of OGC standards for enabling AI geospatial analysis and visualization of data stored in a DGGS. Demonstrated analysis and visualization shall support disaster management requirements.
     
     
   • Demonstrate use of OGC standards for applying two AI approaches with DGGS for disaster response:
     • Enabling an AI chatbot interface to ask questions of DGGS data;
     • Execution of geospatial analysis within DGGS using AI chatbot prompts with results output to the chatbot and mapping user interface.
       
       
3. Maintenance:
   
   
   • Create and maintain the availability of web accessible prototype(s) beyond the project end date for access by all stakeholders and the public.
     
     Suitability and Interoperability: Understand the suitability of geospatial data standards for supporting interoperability between multiple DGGS grid designs for various disaster management (or other) requirements.
     
     
   • Demonstrate the interoperability of independent DGGS using the draft OGC API – DGGS Standard.

Participants in the pilot will propose solutions and prototype implementations through a collaborative innovation process.

Sponsors

The AI-DGGS for Disaster Management is sponsored by

Centre national d’études spatiales
European Space Agency
Natural Resources Canada
United States Geological Survey

Call For Participation

You can find the Call for Participation here. Additionally, the PDF version is accessible here.

Further questions may be sent via the Q&A form.

Responses to the CFP will be due by 20 June 2025, @11:59 PM EDT. All details on eligibility, benefits of participating, and how to apply will be available in the CFP document.