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        Wildfire risk assessment using remote sensing data

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        Publication date
        2022
        Author
        Marinelli, Giuseppe
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        Summary
        Assessing the risk of wildfires over the entire globe can be crucial in avoiding harm to wildlife, economy, properties and humans. This is known to be a challenging task. Here, a machine learning model is trained on a dataset composed of remote sensing data variables such as topography, vegetation and weather. The model is able to assess the risk of fire with a spatial resolution of 1000m/pixel. It achieves optimal results compared to other state-of-the-art architectures. Most of the variables in the dataset are found to be critical for the task, while few were disregarded. Particular focus has been given to collecting data across a variety of landscapes. Specifically, samples from Africa, Australia, Asia, Europe, South America and the US are included. This research shows the potential for deploying global wildfire risk assessment applications.
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        https://studenttheses.uu.nl/handle/20.500.12932/42257
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