Rural road environment segmentation of LiDAR dataset with deep learning

Abstract

Unstructured road segmentation is a key task in self-driving technology and it’s still a challenging problem. Mostly available point cloud datasets focus on data collected from urban areas, and approaches are evaluated for structured roads or urban areas, which has considerable limitations in rural areas such as fails at night, road without boundary lines, and no markings. In this regard, we present a new large-scale aerial LiDAR dataset of rural roads with hand-labeled points spanning 500 km2 of road and nine object categories. Our dataset is the most extensive dataset contains a critical number of expert-verified hand-labeled points for analyzing 3D deep learning algorithms, allowing existing algorithms to shift their focus to unstructured road data. The nature of our data, the annotation methodology, and the performance of existing state-of-the-art algorithms on our dataset are all described in detail. Furthermore, challenges and applications of rural area road semantic segmentation are discussed in detail.