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GeoGPT: Multimodal AI for earth data analysis and sustainable development

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  • Date
    30 March 2026
    Timeframe
    14:00 - 15:00 CEST Geneva
    Duration
    60 minutes
    • Days
      Hours
      Min
      Sec

    GeoGPT is a multimodal Earth AI system developed by scientists around the world and guided by a Governance Committee to support Earth data analysis for sustainability applications. Built on large foundation models, GeoGPT integrates geospatial data, scientific literature, and domain datasets to enable AI-assisted data extraction, map generation, and spatial analysis. In this AI for Good workshop, the GeoGPT system will be introduced and demonstrated to show how multimodal AI can be applied to real-world Earth science tasks. Through hands-on examples, participants will interact with GeoGPT to analyze Earth datasets and explore task-oriented agents for applications such as supporting bird migration and biodiversity studies through data integration and mapping.

    Session Objectives:
    By the end of this workshop, participants will be able to:

    • Explain the purpose and core components of the GeoGPT system as a multimodal AI platform for Earth data analysis and sustainability applications.
    • Use GeoGPT to extract Earth data and generate maps and spatial analyses through browser-based interaction.
    • Apply GeoGPT to real-world Earth science tasks by integrating datasets for use cases such as bird migration and biodiversity studies.

    Recommended Mastery Level / Prerequisites:
    Participants only need a reliable internet connection to get started with GeoGPT. The easiest way to participate is to visit  https://geogpt-sg.zero2x.org/, create an account, and review the GeoGPT User Manual, which introduces the system’s core capabilities for Earth data extraction, map generation, and analysis. For participants who wish to run GeoGPT locally, the model and related resources are available at https://huggingface.co/GeoGPT-Research-Project. This option requires access to a GPU-enabled environment and is recommended for advanced users who want to explore model deployment and customization.
    During the workshop, demonstrations will primarily use browser-based access, and no prior installation is required. Optional references for local or cloud-based execution will be shared for participants interested in deeper experimentation after the session

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