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IEEE International Conference on Green Industrial Electronics and Sustainable Technologies (GIEST)

Quantum-Augmented MobileNet for Efficient Land Classification in Satellite Imagery

N Krishna Santosh, Mallavarapu Vivek, Katru Sri Teja, Bukka Siva Naga Kranthi, K. Adithya

Published Oct 11, 2025• 0 citations
Publisher

Abstract

Accurate land classification from satellite imagery is essential for applications in agriculture, environmental management, and urban planning. Traditional lightweight models such as MobileNet provide computational efficiency but often struggle to capture the fine-grained spatial features necessary for reliable classification. This study proposes a quantum-augmented MobileNet framework that leverages quantum circuits to enhance feature representation. The methodology involves preprocessing satellite images and encoding them into quantum states using parameterized quantum circuits that apply superposition and entanglement to capture complex spatial relationships. These enriched features are then processed through a streamlined MobileNetV2 architecture optimized for multi-class classification across ten land cover categories. The model is trained and evaluated using the EuroSAT dataset consisting of 27,000 images. Experimental outcomes show that the hybrid quantum-classical approach improves model robustness and classification performance without increasing computational overhead. This integration offers a scalable and efficient solution for real-time remote sensing applications, demonstrating the promise of quantum computing in advancing geospatial intelligence.

DOI
10.1109/giest66547.2025.11387560

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