/ScRoadExtractor

Scribble-based Weakly Supervised Deep Learning for Road Surface Extraction from Remote Sensing Images

Primary LanguageC++MIT LicenseMIT

ScRoadExtractor

This repository is the official implementation of Scribble-based Weakly Supervised Deep Learning for Road Surface Extraction from Remote Sensing Images (TGRS 2021)
[arxiv];[paper] image

Dependencies

· Pytorch == 1.1.0
· Python == 3.6.7
· Numpy == 1.16.3
· opencv == 4.1.0
· PyMaxflow == 1.2.12
· scipy == 1.2.1
· Cython == 0.29.13

Structure

Usage

  1. Download dataset and prepare for the code
    The scribbles can be obtained from OpenStreetMap centerlines, GPS traces, or manually annotation through ArcGIS or other software. Also, you can generate skeletonized road lines by thinning road segmentation maps (skimage.morphology.thin). With respect to the implementation of HED Boundary detector, you can refer to the folder boundary_detect. To generate HED masks, download the pre-trained model [network-bsds500.pytorch] by download.bash and run run.py. We also provide the pre-trained model [network-bsds500.pytorch] using the link below. https://pan.baidu.com/s/1AMNnmo7YAk1X3_m8Ky1arw (pwd:0HED)
  2. Road label propagation
    Run road_label_propagation.py to derive proposal masks.
  3. DBNet
    Run train.py for training and run test.py for testing.

Dataset

The Wuhan dataset will be available at http://gpcv.whu.edu.cn/data/data.html in May:)

Feedback

For questions and comments, feel free to contact Yao WEI（email: weiyao@whu.edu.cn)

Citation

If you find our work useful in your research, please cite:
Yao Wei, and Shunping Ji. Scribble-based Weakly Supervised Deep Learning for Road Surface Extraction from Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing 2021.