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

Source: [Categorising the World into Local Climate Zones -- Towards Quantifying Labelling Uncertainty for Machine Learning Models](<https://www.sota2.com/research/paper/68fa7ec51e209131cea97d06>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:57:28.404Z

Authors: Katharina Hechinger, Xiao Xiang Zhu, G\\"oran Kauermann

Published: 2023-09-04T08:41:04.000Z

## Abstract

Image classification is often prone to labelling uncertainty. To generate suitable training data, images are labelled according to evaluations of human experts. This can result in ambiguities, which will affect subsequent models. In this work, we aim to model the labelling uncertainty in the context of remote sensing and the classification of satellite images. We construct a multinomial mixture model given the evaluations of multiple experts. This is based on the assumption that there is no ambiguity of the image class, but apparently in the experts' opinion about it. The model parameters can be estimated by a stochastic Expectation Maximization algorithm. Analysing the estimates gives insights into sources of label uncertainty. Here, we focus on the general class ambiguity, the heterogeneity of experts, and the origin city of the images. The results are relevant for all machine learning applications where image classification is pursued and labelling is subject to humans.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2309.01440>)

[PDF](<https://arxiv.org/pdf/2309.01440.pdf>)