road-extraction's Introduction

Road-Extraction

This repository is the official implementation of Simultaneous Road Surface and Centerline Extraction From Large-Scale Remote Sensing Images Using CNN-Based Segmentation and Tracing.

Our framework consists of three steps: boosting segmentation, multiple starting points tracing, and fusion.

The initial road surface segmentation is achieved with a fully convolutional network (FCN), after which another lighter FCN is applied several times to boost the accuracy and connectivity of the initial segmentation.
In the multiple starting points tracing step, the starting points are automatically generated by extracting the road intersections of the segmentation results, which then are utilized to track consecutive and complete road networks through an iterative search strategy embedded in a convolutional neural network (CNN).
The fusion step aggregates the semantic and topological information of road networks by combining the segmentation and tracing results to produce the final and refined road segmentation and centerline maps.

Pipeline

Results

Road centerline results

Road surface results

Pretrain Model

base segmentation model, pre-tained in massachusetts dataset. Google drive

Code Introduction

evaluate_connectivity.py: evaluate the connectivity of final segmentation result.
base_segmentation/road.py: train or test the model. Modify the parameters before running.

Contributing

Citation

If you find our work useful in your research, please cite:

@ARTICLE{9094008,  
author={Y. {Wei} and K. {Zhang} and S. {Ji}},  
journal={IEEE Transactions on Geoscience and Remote Sensing},   
title={Simultaneous Road Surface and Centerline Extraction From Large-Scale Remote Sensing Images Using CNN-Based Segmentation and Tracing},  
year={2020},  
pages={1-13},}