Remote Sensing and Machine Learning for Environmental Monitoring: Opportunities and Challenges
21Mapping environmental variables continuously across space and time is a key challenge in environmental science and provides essential baseline information to support Sustainable Development Goal 15, which seeks to protect, restore, and promote the sustainable use of terrestrial ecosystems and halt biodiversity loss. Remote sensing and machine learning algorithms have emerged as powerful tools in this domain, enabling the prediction of ecological variables in areas where direct measurements are unavailable, often at a global scale. However, recent research has highlighted significant challenges in using machine learning for large-scale spatial mapping, stemming from the characteristics of remote sensing datasets and the distribution of reference data. This talk aims to raise awareness of these challenges and to propose ideas for addressing them, with the goal of developing more robust environmental spatial datasets that can better support the goals of SDG 15.
The AI for Good Global Summit is the leading action-oriented United Nations platform promoting AI to advance health, climate, gender, inclusive prosperity, sustainable infrastructure, and other global development priorities. AI for Good is organized by the International Telecommunication Union (ITU) – the UN specialized agency for information and communication technology – in partnership with 40 UN sister agencies and co-convened with the government of Switzerland.
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The views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.