Wildfire segmentation analysis from edge computing for on-board real-time alerts using hyperspectral imagery

This paper investigates the opportunity to use artificial intelligence methodologies and edge computing approaches for wildfire detection directly from satellite platforms. The test case for our study is PRISMA (Precursore IperSpettrale della Missione Applicativa-Hyperspectral Precursor of the Application Mission), the Italian hyperspectral satellite launched in 2019 by the Italian Space Agency. This mission provides hyperspectral (HS) images in the spectral range of [0.4,2.5] $\mu$m and an average spectral resolution less than 10 nm. This work reports new results related to the Australian bushfires happened in December 2019 in New South Wales, captured by PRISMA on December 27, 2019. Starting from a one-dimensional convolutional neural network (CNN) discussed in previous authors’ works to perform multiclass classification, this paper primarily deals with the opportunity to use hardware accelerators, namely the Intel Movidius Myriad 2, the Nvidia Jetson TX2, and the Nvidia Jetson Nano, to consider the on-the-edge implementation of the CNN. This study is in line with the current impulse to improve on-board computing capabilities and platform autonomy, setting some of the elements for future satellites or constellations focusing on specific remote sensing tasks to provide real-time reliable early warnings.