  State-of-the-art deep neural networks demonstrate outstanding performance in
semantic segmentation. However, their performance is tied to the domain
represented by the training data. Open world scenarios cause inaccurate
predictions which is hazardous in safety relevant applications like automated
driving. In this work, we enhance semantic segmentation predictions using
monocular depth estimation to improve segmentation by reducing the occurrence
of non-detected objects in presence of domain shift. To this end, we infer a
depth heatmap via a modified segmentation network which generates
foreground-background masks, operating in parallel to a given semantic
segmentation network. Both segmentation masks are aggregated with a focus on
foreground classes (here road users) to reduce false negatives. To also reduce
the occurrence of false positives, we apply a pruning based on uncertainty
estimates. Our approach is modular in a sense that it post-processes the output
of any semantic segmentation network. In our experiments, we observe less
non-detected objects of most important classes and an enhanced generalization
to other domains compared to the basic semantic segmentation prediction.
