Deep learning for large-scale ecosystem monitoring

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  • Date
    6 November 2024
    Timeframe
    17:00 - 18:30
    Duration
    90 minutes (including 30 minutes networking)
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    Tree-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. 

    This live event includes a 30-minute networking event hosted on the AI for Good Neural Network. This is your opportunity to ask questions, interact with the panelists and participants and build connections with the AI for Good community.

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