Myriad efforts have been made over the last 10 years in algorithmic improvements and dataset creation for semantic segmentation tasks. Of late, there have been rapid gains in this field, a subset of visual scene understanding, due mainly to contributions by deep learning methodologies. But deep learning techniques have an Achilles’ heel of consuming vast amounts of annotated data. Here we review some widely used and open, urban semantic segmentation datasets for Self Driving Car applications.
What is Semantic Segmentation?
The task of Semantic Segmentation is to annotate every pixel of an image with an object class. These classes could be “pedestrians, vehicles, buildings, vegetation, sky, void etc” in a self-driving environment. For example, semantic segmentation helps SDCs (Self Driving Cars) discover the driveable areas on an image.