Visualizing Uncertainty in AI for Accident Severity Classification

Uncertainties are frequently encountered in machine learning, especially in classification tasks where the model's output is determined by the probabilities of each class. In geospatial use cases, the challenge is to find appropriate visualizations on geographic maps for these uncertainties, so...

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Bibliographic Details
Published in2024 28th International Conference Information Visualisation (IV) pp. 1 - 7
Main Authors Roussel, Cedric, Bohm, Klaus, Jakobi, Bastian, Vlasov, Alisa, Schmidt, Daniel, Braun, Sebastian, Rolwes, Alexander
Format Conference Proceeding
LanguageEnglish
Published IEEE 22.07.2024
Subjects
Accidents
Boosting
Data visualization
GeoAI
Geospatial analysis
Image color analysis
Semiotics
Symbols
Uncertainties
Uncertainty
Visual Semiotics
Visualization
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Summary:Uncertainties are frequently encountered in machine learning, especially in classification tasks where the model's output is determined by the probabilities of each class. In geospatial use cases, the challenge is to find appropriate visualizations on geographic maps for these uncertainties, so that domain experts know when to trust the model. This study utilizes the uncertainties of an extreme gradient boosting machine to classify traffic accident severity and presents various visualization profiles. These visualizations are the outcome of an experimental approach. Each profile has its own advantages but may also encounter issues such as overlapping data points or the determination of what uncertain means. The most frequently used visualization types are symbols and colors.
ISSN:2375-0138
DOI:10.1109/IV64223.2024.00030