Dipartimento di Ingegneria informatica, automatica e gestionale

Forest fires cost the world an estimated value of 200 Billion dollars annually in damages. Furthermore, the main concerns are not only monetary as the vanishment of the carbon-dioxide soaking forests further exacerbates climate change. This paper presents a predictor system based on deep convolutional neural network to predict the risk level of wildfire from satellite data. The proposed Neural Network has an encoder-decoder architecture that allows to provide emergency operators with a pixel-wise fire risk prediction of a given area, allowing precise preventive interventions. The dataset utilised for the training has been generated from publicly available sources as a set of raster images, including several of the most significant satellite products. The paper also proposes a customised loss function for the training of the network and several statistical metrics to establish its performances and validate the reliability of the system. A proof of concept demonstration is discussed for two different case studies: the island of Sicily and an area in California.