Wildfire Detection Based on Satellite Images Using Deep Learning

The extreme weather caused by climate changes has caused a lot of serious damages across the globe. Wildfire is one of the disasters that increasingly threaten the public safety around the world, such as the wildfire in Canada back in 2023, causing the massive ash flow and affected multiple areas around including cities in the neighboring countries. Researchers have developed various simulation models to predict the wildfire occurrence and its spread. However, the prediction accuracy is limited due to the unavailability of the real time parameters, such as the fuel and GIS data of the fire field. With the abundance of satellite image data, deep learning has attracted researchers' attention for the wildfire detection. In this paper, we propose a deep learning method using Convolutional Neural Networks (CNNs) to predict the wildfire based on the satellite image data set. The experimental results show the achieved prediction accuracy reaches up to 96%, validating the effectiveness of the proposed model.