# MSc Thesis on Wildfire risk assessment using remote sensing data

## Introduction
Hi to whoever ends up here on this webpage. Here there is the material I used for my master thesis. You're free to use the code and check what I have done. 
> What have I done in a few words? I have built a machine learning model (CNN) capable of assessing the risk of a wildfire over the entire globe using remote sensing data.

## Abstract
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.


# You can read more [here](https://github.com/BeppeMarnell/MSc-Thesis-Wildfire-prediction)

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license: apache-2.0
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