Semantic Segmentation Datasets for Autonomous Driving

<p>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&nbsp;<a href="https://hackernoon.com/tagged/learning" rel="noopener ugc nofollow" target="_blank">learning</a>&nbsp;techniques have an Achilles&rsquo; 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.</p> <h1>What is Semantic Segmentation?</h1> <p>The task of Semantic Segmentation is to annotate every pixel of an image with an object class. These classes could be &ldquo;pedestrians, vehicles, buildings, vegetation, sky, void etc&rdquo; in a self-driving environment. For example, semantic segmentation helps SDCs (Self Driving Cars) discover the driveable areas on an image.</p> <p><a href="https://medium.com/hackernoon/semantic-segmentation-datasets-for-autonomous-driving-1182ebd2aff0"><strong>Read More</strong></a></p>