Awesome

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

dataset

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.