Volcanic Clouds Detection through QCNN and Geostationary Satellite Multispectral Imagery

2026-07-29

AI Summary

Accurate identification of volcanic clouds remains challenging due to their spectral similarity to meteorological clouds, highly variable eruption morphologies, and the limited spectral resolution of geostationary satellites. This work proposes a novel hybrid quantum-classical convolutional architecture for volcanic cloud detection in remote sensing, integrating quantum convolutional neural networks (QCNNs) with classical CNNs to classify SEVIRI multispectral imagery into categories containing volcanic ash, SO₂, or their mixtures. Two QCNN variants—employing 2 and 4 qubits, respectively—are evaluated experimentally. Results demonstrate that the proposed approach effectively identifies volcanic cloud scenarios and outperforms purely classical models, thereby validating the practical potential and feasibility of quantum machine learning in Earth observation applications.

Abstract

Recent advances in quantum computing are opening new possibilities for Earth Observation (EO) data analysis. Quantum machine learning (QML) approaches offer novel ways to process information by exploiting quantum phenomena such as superposition and entanglement. These capabilities have motivated the exploration of whether quantum-enhanced models can address long-standing challenges in satellite remote sensing, where complex spectral and spatial signals often require sophisticated feature extraction. Among various fields of application, EO data allow the global monitoring of volcanic clouds and are crucial for aviation safety, hazard assessment, real-time eruption response, and evaluation of volcanic impacts on climate. Yet accurate detection of volcanic clouds remains difficult due to their similarity with meteorological clouds, the variability of eruption signatures, and the coarse spectral sampling of geostationary sensors. In this work, the potential of hybrid quantum convolutional neural networks (QCNNs) for the classification of satellite images containing volcanic clouds was investigated. These architectures integrate quantum computational layers into a classical convolutional framework. Two QCNN variants (with 2 and 4 qubits) have been considered to evaluate their ability to classify a dataset of SEVIRI images, including scenes with volcanic clouds (composed of ash, $SO_2$, or mixed components) as well as non-volcanic backgrounds. Finally, the performance of the hybrid QCNN models was compared with that of purely classical architectures.

Problem

• volcanic clouds detection
• geostationary satellite imagery
• spectral variability
• cloud discrimination
• remote sensing challenges

Innovation

• Quantum Convolutional Neural Network
• Volcanic Cloud Detection
• Geostationary Satellite Imagery
• Quantum Machine Learning
• SEVIRI