Automated classification of fuel types using roadside images via deep learning

Abstract

There is an urgent need to develop new data-driven methods for assessing wildfire-related risks in large areas susceptible to such risks. To assess these risks, one of the critical parameters to analyse is fuel. This research note presents a framework for classifying fuels through the analysis of roadside images to complement the current practice of assessing fuels through aerial images and visual inspections. Some of the most prevalent fuel types in North America were considered for automated classification, including grasses, shrubs and timbers. A framework was developed using convolutional neural networks (CNNs), which can automate the process of fuel classification. Various pre-trained neural networks were examined and the best network in terms of time efficiency and accuracy was identified, and had ~94% accuracy in identifying the chosen fuel types. This framework was initially applied to street view images collected from Google Earth. Indeed, the results showed that the framework has the potential for application for fuel classification using roadside images, and this makes it suitable for crowdsensing-based fuel mapping for wildfire risk assessment, which is the future goal of this research.