Volcanic ash delimitation using Artificial Intelligence based on Pix2Pix

TL;DR

This paper introduces a novel AI-based method using Pix2Pix GAN to accurately delineate volcanic ash clouds from satellite images, offering a faster and more accessible alternative to traditional techniques.

Contribution

It applies the Pix2Pix generative adversarial network to satellite imagery for volcanic ash cloud delimitation, demonstrating improved accuracy and efficiency over conventional methods.

Findings

Satisfactory ash cloud delineation achieved

Model is applicable globally and in real-time scenarios

Outperforms traditional image processing techniques

Abstract

Volcanic eruptions emit ash that can be harmful to human health and cause damage to infrastructure, economic activities and the environment. The delimitation of ash clouds allows to know their behavior and dispersion, which helps in the prevention and mitigation of this phenomenon. Traditional methods take advantage of specialized software programs to process the bands or channels that compose the satellite images. However, their use is limited to experts and demands a lot of time and significant computational resources. In recent years, Artificial Intelligence has been a milestone in the computational treatment of complex problems in different areas. In particular, Deep Learning techniques allow automatic, fast and accurate processing of digital images. The present work proposes the use of the Pix2Pix model, a type of generative adversarial network that, once trained, learns the mapping of input images to output images. The architecture of such a network consisting of a generator and a discriminator provides the versatility needed to produce black and white ash cloud images from multispectral satellite images. The evaluation of the model, based on loss and accuracy plots, a confusion matrix, and visual inspection, indicates a satisfactory solution for accurate ash cloud delineation, applicable in any area of the world and becomes a useful tool in risk management.