Fossil charcoal particle identification and classification by two convolutional neural networks

Fire is a significant natural and cultural phenomenon, affecting spatial scales from local to global, and is represented in most palaeoenvironmental records by fossil charcoal. Analysis is resource-intensive and requires high-level expert knowledge. This study is a preliminary investigation of the application of artificial neural networks to fossil charcoal particle analysis, utilizing a U-Net variant for charcoal particle identification and VGG for particle classification by morphology. Both neural networks performed well, reaching ∼96% accuracy for particle identification and ∼75% accuracy for classification. Future work will include expansion of the training dataset, including total number of particles and number of sites. The development and application of this automated system will increase the efficiency of fossil charcoal analysis. •Convolutional neural networks can identify and classify fossil charcoal particles.•Neural network U-Net variant achieved ∼96% accuracy for charcoal identification.•Neural network VGG achieved ∼75% accuracy for charcoal classification.