  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.
