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

arXiv: https://arxiv.org/abs/2503.21510 · HTML: https://mlsanity.com/paper/2503.21510/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 46 references that are in the map.
- First posted on arXiv 27 Mar 2025.

## Builds on (most cited first, 10 of 10)

- [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019)
- [Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Users](https://mlsanity.com/paper/2007.06823/) (2020)
- [Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks](https://mlsanity.com/paper/2002.10118/) (2020)
- [Macro F1 and Macro F1](https://mlsanity.com/paper/1911.03347/) (2019)
- [Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife](https://mlsanity.com/paper/1311.4555/) (2013)
- [Continental-scale land cover mapping at 10 m resolution over Europe (ELC10)](https://mlsanity.com/paper/2104.10922/) (2021)
- [Aleatoric uncertainty for Errors-in-Variables models in deep regression](https://mlsanity.com/paper/2105.09095/) (2021)
- [Trustworthy Artificial Intelligence in the Context of Metrology](https://mlsanity.com/paper/2406.10117/) (2024)
- [Quadratic Discriminant Analysis by Projection](https://mlsanity.com/paper/2108.09005/) (2021)
- [Analytical results for uncertainty propagation through trained machine learning regression models](https://mlsanity.com/paper/2404.11224/) (2024)

## Built on by (most cited first, 1 of 1)

- [A metrological framework for uncertainty evaluation in machine learning classification models](https://mlsanity.com/paper/2504.03359/) (2025)

## Abstract (arXiv)

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. We take the specific case of a Bayesian quadratic discriminant analysis (BQDA) model, and apply it to metrological land cover datasets from Copernicus Sentinel-2 from 2020 and 2021. We benchmark the performance of the model against more popular classification models used in land cover maps such as random forests and neural networks. To validate and assess the generalisability of such a model, we also run simulations over synthetic classification data, varying distribution type and strength of the input measurement noise. We find for both real and synthetic data, the BQDA model presented is more trustworthy, in the sense that it is more interpretable, explicitly models the input measurement uncertainty, and maintains predictive performance of class probability outputs across datasets over different domains and sizes, whilst also being more computationally efficient.