An uncertainty-aware Bayesian framework for machine learning classification models: A case study in land cover classification

ArXi:2503.21510v3 Announce Type: replace Ensuring that predictions of machine learning (ML) classification models are accompanied by uncertainty estimates is one of the main pillars of trustworthy AI. Current research in uncertainty quantification focuses mainly on epistemic uncertainty of the ML model, but rarely takes account of input measurement uncertainty, which is vital for traceability in metrology. In this work we propose a Bayesian framework for generative ML classification models that takes account of input measurement uncertainty.