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Semantic understanding of roadways is a key enabling factor for safeautonomous driving. However, existing autonomous driving datasets providewell-structured urban roads while ignoring unstructured roadways containingdistress, potholes, water puddles, and various kinds of road patches i.e.,earthen, gravel etc. To this end, we introduce Road Region Segmentation dataset(R2S100K) -- a large-scale dataset and benchmark for training and evaluation ofroad segmentation in aforementioned challenging unstructured roadways. R2S100Kcomprises 100K images extracted from a large and diverse set of video sequencescovering more than 1000 KM of roadways. Out of these 100K privacy respectingimages, 14,000 images have fine pixel-labeling of road regions, with 86,000unlabeled images that can be leveraged through semi-supervised learningmethods. Alongside, we present an Efficient Data Sampling (EDS) basedself-training framework to improve learning by leveraging unlabeled data. Ourexperimental results demonstrate that the proposed method significantlyimproves learning methods in generalizability and reduces the labeling cost forsemantic segmentation tasks. Our benchmark will be publicly available tofacilitate future research at https://r2s100k.github.io/.

R2S100K: Road-Region Segmentation Dataset For Semi-Supervised Autonomous Driving in the Wild