This work presents an automated method for detecting and monitoring volcanic islands in the Tonga archipelago using Sentinel-2 satellite imagery. The method is able to detect newly created islands, as well as an increase in island size, a possible precursor to an explosion due to magma chamber inflation. At its core, the method combines a U-Net-type convolutional neural network (CNN) for semantic segmentation with a custom change detection algorithm, enabling the identification of land–water boundaries and the tracking of volcanic island dynamics. The algorithm analyzes morphological changes through image comparison and Intersection over Union (IoU), capturing the emergence, disappearance, and evolution of volcanic islands. The segmentation model, trained on a custom dataset of Pacific Ocean imagery, achieved an IoU score of 97.36% on the primary test dataset and 83.54% on a subset of challenging cases involving small, recently formed volcanic islands. Generalization capability was validated using the SNOWED dataset, where the segmentation model attained an IoU of 81.02%. Applied to recent volcanic events, the workflow successfully detected changes in island morphology and provided time-series analyses. Practical feasibility of the methodology was assessed by testing it on a large region in Tonga, using an HPC cluster. This system offers potential applications for geophysical studies and navigation safety in volcanically active regions.
