  Road surface extraction from remote sensing images using deep learning
methods has achieved good performance, while most of the existing methods are
based on fully supervised learning, which requires a large amount of training
data with laborious per-pixel annotation. In this paper, we propose a
scribble-based weakly supervised road surface extraction method named
ScRoadExtractor, which learns from easily accessible scribbles such as
centerlines instead of densely annotated road surface ground-truths. To
propagate semantic information from sparse scribbles to unlabeled pixels, we
introduce a road label propagation algorithm which considers both the
buffer-based properties of road networks and the color and spatial information
of super-pixels. The proposal masks generated from the road label propagation
algorithm are utilized to train a dual-branch encoder-decoder network we
designed, which consists of a semantic segmentation branch and an auxiliary
boundary detection branch. We perform experiments on three diverse road
datasets that are comprised of highresolution remote sensing satellite and
aerial images across the world. The results demonstrate that ScRoadExtractor
exceed the classic scribble-supervised segmentation method by 20% for the
intersection over union (IoU) indicator and outperform the state-of-the-art
scribble-based weakly supervised methods at least 4%.
