Improving wildland fire spread prediction using deep U-Nets
Fadoua Khennou, Moulay A. Akhloufi
Abstract
Forest fires are able to cause significant damage to humans and the earth's fauna and flora. If a fire is not detected and extinguished before it spreads, it can have disastrous results. In addition to satellite images, recent studies have shown that exploring both weather and topography characteristics is crucial for effectively predicting the propagation of wildfires. In this paper, we present FU-NetCastV2, a deep learning convolutional neural network for fire spread and burned area mapping. This algorithm predicts which areas around wildfires are at high risk of future spread. With an accuracy of 94.6% and an AUC of 97.7%, the model surpassed the literature by 3.7% and exhibited a 1.9% improvement over our previous model. The proposed approach was implemented using consecutive forest wildfire perimeters, satellite images, Digital Elevation Model maps, aspect, slope and weather data.