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Quantum-Enhanced Vision Transformer for Flood Detection using Remote Sensing Imagery

Soumyajit Maity, Behzad Ghanbarian

Abstract

Reliable flood detection is critical for disaster management, yet classical deep learning models often struggle with the high-dimensional, nonlinear complexities inherent in remote sensing data. To mitigate these limitations, we introduced a novel Quantum-Enhanced Vision Transformer (ViT) that synergizes the global context-awareness of transformers with the expressive feature extraction capabilities of quantum computing. Using remote sensing imagery, we developed a hybrid architecture that processes inputs through parallel pathways, a ViT backbone and a quantum branch utilizing a 4-qubit parameterized quantum circuit for localized feature mapping. These distinct representations were fused to optimize binary classification. Results showed that the proposed hybrid model significantly outperformed a classical ViT baseline, increased overall accuracy from 84.48% to 94.47% and the F1-score from 0.841 to 0.944. Notably, the quantum integration substantially improved discriminative power in complex terrains for both class. These findings validate the potential of quantum-classical hybrid models to enhance precision in hydrological monitoring and earth observation applications.

Quantum-Enhanced Vision Transformer for Flood Detection using Remote Sensing Imagery

Abstract

Reliable flood detection is critical for disaster management, yet classical deep learning models often struggle with the high-dimensional, nonlinear complexities inherent in remote sensing data. To mitigate these limitations, we introduced a novel Quantum-Enhanced Vision Transformer (ViT) that synergizes the global context-awareness of transformers with the expressive feature extraction capabilities of quantum computing. Using remote sensing imagery, we developed a hybrid architecture that processes inputs through parallel pathways, a ViT backbone and a quantum branch utilizing a 4-qubit parameterized quantum circuit for localized feature mapping. These distinct representations were fused to optimize binary classification. Results showed that the proposed hybrid model significantly outperformed a classical ViT baseline, increased overall accuracy from 84.48% to 94.47% and the F1-score from 0.841 to 0.944. Notably, the quantum integration substantially improved discriminative power in complex terrains for both class. These findings validate the potential of quantum-classical hybrid models to enhance precision in hydrological monitoring and earth observation applications.
Paper Structure (10 sections, 4 equations, 6 figures, 2 tables)

This paper contains 10 sections, 4 equations, 6 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Dataset
  4. Dataset Preprocessing
  5. Proposed Model Architecture
  6. Quantum Feature Pathway
  7. ViT Pathway
  8. Results and Discussion
  9. Conclusion
  10. Acknowledgment

Figures (6)

  • Figure 1: The framework proposed in this study.
  • Figure 2: Class distribution of SEN12-FlOOD dataset. Here label 0 refers to "Non-Flooded" class and label 1 refers to "Flooded" class.
  • Figure 3: GeoTIFF files (left) before and (right) after pre-processing.
  • Figure 4: Proposed quantum-enhanced vision transformer model architecture.
  • Figure 5: The confusion matrix for the classical ViT baseline.
  • ...and 1 more figures