<ul><li>Land cover classification involves the production of land cover maps using remote sensing imagery.</li><li>A new Bayesian classification framework is proposed to incorporate input measurement uncertainty in land cover classification.</li><li>The framework applies Bayesian quadratic discriminant analysis to land cover datasets from Copernicus Sentinel-2.</li><li>The Bayesian models provide better interpretability, explicitly model input measurement uncertainty, and maintain predictive performance.</li></ul>

Uncertainty-aware Bayesian machine learning modelling of land cover classification

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