Rethinking labelling in road segmentation

Abstract

ABSTRACTDeep models are extremely data hungry. Their success is being driven by the availability of large amounts of data for training. For semantic segmentation tasks on aerial and satellite imagery, a major dilemma at present is that it still relies heavily on manual labelling of data. Among these tasks, the semantic segmentation of road is special since it is possible to use auxiliary data, such as GPS track data, to automatically label data. For a better understanding of this possibility, this paper proposes to rethink some basic issues of labelling approaches for roads.We experimentally investigated the unavoidable class imbalance problem in road segmentation tasks through simulated and real datasets and quantitatively show that class imbalance has a serious detrimental impact on deep model’s generalization performance. We also observed that the detrimental impact even outweighs the benefits of strictly annotating roads – expanding road labels can give deep networks better segmentation accuracy, even though the segmentation location is no longer the edge of the road. We think this is because the impact of class imbalance is much overwhelming than the sensitivity of DNN on the edges of real roads. This finding is valuable for supporting the use of centreline-based approaches in place of edge-based approaches in some applications for better cost-effective solutions.We proposed a guided Random Sample Consensus (RANSAC) algorithm to determine the optimal expansion ratio of road label. On these bases, we further proposed a general framework to combine two networks to achieve better performance than the state-of-the-art performance of using alone. We attribute this to the alleviation of the class imbalance problem because simply cascading the two networks does not achieve the purpose of improving accuracy in our experiments. We believe that this work is enlightening for studies of road segmentation.