Enhancing detection of volcanic ash clouds from space with convolutional neural networks

Abstract

Volcanic ash cloud detection is a crucial component of volcano monitoring and a valuable tool for investigating ash cloud dispersion, which is paramount for enhancing the safety of human settlements and air traffic. The latest generation of high-resolution satellite sensors (e.g., EUMETSAT MSG Spinning Enhanced Visible and InfraRed Imager, SEVIRI) provides radiometric estimates for monitoring volcanic clouds on a global scale efficiently and timely. However, these radiometric intensities are not always discriminative enough to detect volcanic ash clouds due to the spectral limitations of these instruments and the complex nature of some volcanic clouds, such as low concentration resulting in an averaged detected radiometric estimate comparable to the background. Here, we evaluate the ability of a Convolutional Neural Network (CNN) to detect and track the dispersion of volcanic ash clouds into the atmosphere, exploiting a variety of spatial and spectral intensity information mainly coming from SEVIRI Ash RGB images. We train a deep CNN model through transfer learning, and demonstrate that the trained models overcome the limitations of algorithms based solely on pixel intensity, whether traditional or machine learning, resulting in increased performance compared to other methods. We illustrate the operation of this model using the paroxysmal explosive events that occurred at Mt. Etna between 2020 and 2022.