  Hazard detection is critical for enabling autonomous landing on planetary
surfaces. Current state-of-the-art methods leverage traditional computer vision
approaches to automate identification of safe terrain from input digital
elevation models (DEMs). However, performance for these methods can degrade for
input DEMs with increased sensor noise. At the same time, deep learning
techniques have been developed for various applications. Nevertheless, their
applicability to safety-critical space missions has been often limited due to
concerns regarding their outputs' reliability. In response to this background,
this paper proposes an uncertainty-aware learning-based method for hazard
detection and landing site selection. The developed approach enables reliable
safe landing site selection by: (i) generating a safety prediction map and its
uncertainty map together via Bayesian deep learning and semantic segmentation;
and (ii) using the generated uncertainty map to filter out the uncertain pixels
in the prediction map so that the safe landing site selection is performed only
based on the certain pixels (i.e., pixels for which the model is certain about
its safety prediction). Experiments are presented with simulated data based on
a Mars HiRISE digital terrain model and varying noise levels to demonstrate the
performance of the proposed approach.
