  Landslides are a recurring, widespread hazard. Preparation and mitigation
efforts can be aided by a high-quality, large-scale dataset that covers global
at-risk areas. Such a dataset currently does not exist and is impossible to
construct manually. Recent automated efforts focus on deep learning models for
landslide segmentation (pixel labeling) from satellite imagery. However, it is
also important to characterize the uncertainty or confidence levels of such
segmentations. Accurate and robust uncertainty estimates can enable low-cost
(in terms of manual labor) oversight of auto-generated landslide databases to
resolve errors, identify hard negative examples, and increase the size of
labeled training data. In this paper, we evaluate several methods for assessing
pixel-level uncertainty of the segmentation. Three methods that do not require
architectural changes were compared, including Pre-Threshold activations,
Monte-Carlo Dropout and Test-Time Augmentation -- a method that measures the
robustness of predictions in the face of data augmentation. Experimentally, the
quality of the latter method was consistently higher than the others across a
variety of models and metrics in our dataset.
