Quantum Machine Learning for Land Cover Classification: Evaluating Variational Quantum Classifiers Using Sentinel-2 Imagery - Silva, Carlos Henrique de Moura (Anais do Simpósio Brasileiro de Computação e Comunicação Quânticas (SBCCQ), 2026)
Land cover classification using remote sensing data is essential for environmental monitoring and agricultural management. This study evaluates the feasibility of applying a Variational Quantum Classifier (VQC) to distinguish soybean fields from forest formations using Sentinel-2 imagery. Spectral bands B2, B3, B4 (10 m resolution) and annual mean NDVI were used as input features. The quantum model, implemented in Qiskit’s simulator, was compared with a classical neural network trained on the same dataset. While the classical model achieved higher accuracy (above 90% for both classes), the quantum classifier demonstrated competitive performance, particularly for forest identification. The results highlight current limitations of quantum classifiers in complex agricultural landscapes, while indicating future potential as quantum hardware and algorithms evolve.