RoadNet

A multi-task benchmark dataset for the paper: RoadNet: Learning to Comprehensively Analyze Road Networks in Complex Urban Scenes from High-Resolution Remotely Sensed Images, IEEE Transactions on Geoscience and Remote Sensing (TGRS, IF: 5.63), 2019. [paper] | [code] | [dataset]

1.Dataset

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We collected several typical urban areas of Ottawa, Canada from Google Earth. The images are with 0.21m spatial resolution per pixel (zoom level 19).

Please note that we do not own the copyrights to these original satellite images. Their use is RESTRICTED to non-commercial research and educational purposes.

1.1.Download

Download link:

• BaiduYun（Password: h2zt）
• GoogleDrive

1.2.Training and Testing

Training files:

• 2,3,4,5,6,7,8,9,10,11,12,13,14,15

Testing files:

• 1,16,17,18,19,20

1.3.Annotations

We take an example with the folder "1":

Filename	Explaination
Ottawa-1.tif	original image
segmentation.png	manual annotaion of road surface
edge.png	manual annotation of road edge
centerline.png	manual annotation of road centerline
extra.png	roughly mark the heterogeneous regions with a single pixel width brush (red)
extra-Ottawa-1.tif	the Ottawa-1.tif is overlaid with the extra.png

2.Visualization of Results

3.Reference

Please cite this paper if you use this dataset:

@article{liu2019roadnet,
  title={RoadNet: Learning to Comprehensively Analyze Road Networks in Complex Urban Scenes from High-Resolution Remotely Sensed Images},
  author={Liu, Yahui and Yao, Jian and Lu, Xiaohu and Xia, Menghan and Wang, Xingbo and Liu, Yuan},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  volume={57},
  number={4},
  pages={2043--2056},
  year={2019},
  doi={10.1109/TGRS.2018.2870871}
}

If you have any questions, please contact me: yahui.cvrs AT gmail.com without hesitation.