Forest fire spread prediction using deep learning

Nowadays, we are facing a tremendous increase in the number of forest fires around the world. While in 2010, the world had 3.92Gha of forest cover, covering 30% of its land area, in 2019, there was a loss of forest cover of 24.2Mha according to the Global Forest Watch institute. These fires can take different forms depending on the characteristics of the vegetation and the climatic conditions in which they develop. To better manage this and reduce human, economic and environmental consequences, it is crucial to consider artificial intelligence as a mean to predict the new probable burned area. In this paper, we present FU-NetCast, a deep learning model based on U-Net, past wildfires events and weather data. Our approach uses an intelligent model to study forest fire spread over a period of 24 hours. The model achieved an accuracy of 92.73% and an AUC of 80% using 120 wildfire perimeters, satellite images, Digital Elevation Model maps and weather data.