Topo-boundary: A Benchmark Dataset on Topological Road-boundary Detection Using Aerial Images for Autonomous Driving
Zhenhua Xu, Yuxiang Sun, Ming Liu

TL;DR
This paper introduces Topo-boundary, a comprehensive benchmark dataset of aerial images for offline topological road-boundary detection, along with evaluation metrics and baseline methods to advance autonomous driving safety and mapping.
Contribution
It provides the first large-scale publicly available dataset for offline topological road-boundary detection using aerial images, along with new evaluation metrics and baseline algorithms.
Findings
The dataset contains 25,295 high-resolution aerial images with labels for multiple sub-tasks.
A new entropy-based metric improves connectivity evaluation by handling noise and outliers.
The proposed imitation-learning baseline outperforms existing segmentation and graph-based methods.
Abstract
Road-boundary detection is important for autonomous driving. It can be used to constrain autonomous vehicles running on road areas to ensure driving safety. Compared with online road-boundary detection using on-vehicle cameras/Lidars, offline detection using aerial images could alleviate the severe occlusion issue. Moreover, the offline detection results can be directly employed to annotate high-definition (HD) maps. In recent years, deep-learning technologies have been used in offline detection. But there still lacks a publicly available dataset for this task, which hinders the research progress in this area. So in this paper, we propose a new benchmark dataset, named \textit{Topo-boundary}, for offline topological road-boundary detection. The dataset contains 25,295 -sized 4-channel aerial images. Each image is provided with 8 training labels for different sub-tasks.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAutomated Road and Building Extraction · Remote Sensing and LiDAR Applications · Advanced Neural Network Applications
