AI for humanity: Using earth observation for solving global challenges

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  • Date
    3 November 2025
    Timeframe
    17:00 - 18:00 CET Geneva
    Duration
    60 minutes

    In this talk, Kelsey will cover two projects related to tackling some of the humanity’s biggest challenges using AI and earth observation (EO). The first project targets quality education through work done on the Giga project. In December 2020, the United Nations Children’s Fund (UNICEF) and the International Telecommunication Union (ITU) reported that two-thirds (1.3 billion) of the world’s school-age children do not have internet connection in their homes. To combat this, the Giga Initiative was created, aiming to connect every school to the internet by 2030. Where traditionally, policy makers rely on prohibitively costly and timely surveys to capture the data required for a clear understanding of a country’s digital infrastructure, we present our methodology in collaboration with the ESA phi lab to leveraging Earth Observation data with machine learning to predict internet connectivity in schools in the Global South. Our work tackles the digital divide, leveraging the latest geospatial foundation models to support internet connectivity mapping from space. Our second project targets climate action. We highlight our sub-seasonal landslide impact forecasting model. The work was deployed in Nepal by the UN Humanitarian Country Team to support disaster preparedness in the region, using freely-available EO data.

     

    Learning Objectives
    By the end of this session, participants will be able to:

    • Identify practical examples of how open EO datasets can be used in addressing sustainability and development challenges.
    • Assess the advantages, limitations, and ethical implications of using machine learning with EO data for global development and disaster preparedness.
    • Critically appraise how AI4EO methods can support policymakers and humanitarian organizations in decision-making.

     

    Recommended Mastery Level / Prerequisites:

    Master’s (EQF 7)

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