  Semantic segmentation aims to robustly predict coherent class labels for
entire regions of an image. It is a scene understanding task that powers
real-world applications (e.g., autonomous navigation). One important
application, the use of imagery for automated semantic understanding of
pedestrian environments, provides remote mapping of accessibility features in
street environments. This application (and others like it) require detailed
geometric information of geographical objects. Semantic segmentation is a
prerequisite for this task since it maps contiguous regions of the same class
as single entities. Importantly, semantic segmentation uses like ours are not
pixel-wise outcomes; however, most of their quantitative evaluation metrics
(e.g., mean Intersection Over Union) are based on pixel-wise similarities to a
ground-truth, which fails to emphasize over- and under-segmentation properties
of a segmentation model. Here, we introduce a new metric to assess region-based
over- and under-segmentation. We analyze and compare it to other metrics,
demonstrating that the use of our metric lends greater explainability to
semantic segmentation model performance in real-world applications.
