Simultaneous Road Surface and Centerline Extraction from Large-Scale Remote Sensing Images Using CNN-Based Segmentation and Tracing

Overview

We develop a novel deep learning-based multistage framework to accurately extract the road surface and road centerline simultaneously. The framework consists of three steps: boosting segmentation, multiple starting points tracing, and fusion. We evaluated our method utilizing three data sets covering various road situations in more than 40 cities around the world. The results demonstrate our method’s performance exceeded the other methods by 7% and 40% for the connectivity indicator for road surface segmentation and for the completeness indicator for centerline extraction, respectively.

If you find it is helpful in your research, cite our paper (Wei, Y., Zhang, K., & Ji, S. Simultaneous Road Surface and Centerline Extraction from Large-Scale Remote Sensing Images Using CNN-Based Segmentation and Tracing. IEEE Transactions on Geoscience and Remote Sensing. vol. 58, no. 12, pp. 8919-8931, Dec. 2020). Please note that the code can be used in any academic study however the commercial application is not allowed as the system is protected by a Chinese Patent with Number 201911228166.8.

Code

Download the network code. Click HERE to Download

Dependencies

Usage

Modify the parameters before running. More details, please contact weiyao@whu.edu.cn or zhangkai11@whu.edu.cn.