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Image classification is often prone to labelling uncertainty. To generatesuitable training data, images are labelled according to evaluations of humanexperts. This can result in ambiguities, which will affect subsequent models.In this work, we aim to model the labelling uncertainty in the context ofremote sensing and the classification of satellite images. We construct amultinomial mixture model given the evaluations of multiple experts. This isbased on the assumption that there is no ambiguity of the image class, butapparently in the experts' opinion about it. The model parameters can beestimated by a stochastic Expectation Maximization algorithm. Analysing theestimates gives insights into sources of label uncertainty. Here, we focus onthe general class ambiguity, the heterogeneity of experts, and the origin cityof the images. The results are relevant for all machine learning applicationswhere image classification is pursued and labelling is subject to humans.

Categorising the World into Local Climate Zones -- Towards Quantifying Labelling Uncertainty for Machine Learning Models