Uncertainty Map

Uncertainty maps represent a crucial advancement in various fields by providing a quantitative measure of confidence associated with predictions or estimations within a model's output. Current research focuses on integrating uncertainty estimation into diverse models, including deep neural networks, Gaussian processes, and particle filters, to improve robustness and reliability in applications such as image segmentation, autonomous navigation, and network quality mapping. This focus on quantifying uncertainty enhances the trustworthiness and interpretability of model outputs, leading to improved decision-making in safety-critical applications and more efficient data collection strategies.