  Deep neural networks (DNNs) have been successfully applied to earth
observation (EO) data and opened new research avenues. Despite the theoretical
and practical advances of these techniques, DNNs are still considered black box
tools and by default are designed to give point predictions. However, the
majority of EO applications demand reliable uncertainty estimates that can
support practitioners in critical decision making tasks. This work provides a
theoretical and quantitative comparison of existing uncertainty quantification
methods for DNNs applied to the task of wind speed estimation in satellite
imagery of tropical cyclones. We provide a detailed evaluation of predictive
uncertainty estimates from state-of-the-art uncertainty quantification (UQ)
methods for DNNs. We find that predictive uncertainties can be utilized to
further improve accuracy and analyze the predictive uncertainties of different
methods across storm categories.
