Deep learning for large-scale ecosystem monitoring
12Tree-based ecosystems play a critical role in carbon sequestration, biodiversity, and the production of timber and food. Understanding how these ecosystems are influenced by climate change and human management necessitates a more comprehensive global characterization of woody resources. Recent advancements in remote sensing and machine learning-based computer vision have made such detailed analysis possible. For example, applying deep learning techniques to high-resolution satellite imagery enables the large-scale mapping of individual trees. The biomass of each tree, and thus its carbon content, can be estimated from the crown size using data-derived allometric equations. These equations are non-decreasing, and their integration into machine learning models not only enhances scientific validity but also acts as a strong regularizer.
In this session, we will discuss a specific application in Rwanda, where assessing tree carbon stocks supports the development of strategies to achieve the country's sustainable development goals. The presentation will conclude with an introduction to recently released open-source image datasets that aid in training machine learning models for various environmental monitoring tasks.
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.