https://github.com/yhlleo/RoadNet

RoadNet: A Multi-task Benchmark Dataset for Road Detection, TGRS.
https://github.com/yhlleo/RoadNet

centerline-detection dataset edge-detection image-segmentation multi-task-learning road-detection

Last synced: 11 days ago
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RoadNet: A Multi-task Benchmark Dataset for Road Detection, TGRS.

• Host: GitHub
• URL: https://github.com/yhlleo/RoadNet
• Owner: yhlleo
• Created: 2018-11-13T13:19:23.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2023-03-17T02:43:30.000Z (over 1 year ago)
• Last Synced: 2024-03-06T10:33:46.063Z (4 months ago)
• Topics: centerline-detection, dataset, edge-detection, image-segmentation, multi-task-learning, road-detection
• Homepage: https://ieeexplore.ieee.org/document/8506600
• Size: 18.9 MB
• Stars: 92
• Watchers: 2
• Forks: 19
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

Lists

• awesome-satellite-imagery-datasets - **RoadNet**

README

        
# 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](https://ieeexplore.ieee.org/document/8506600), IEEE Transactions on Geoscience and Remote Sensing (TGRS, IF: 5.63), 2019. [[paper]](./RoadNet-TGRS-2019.pdf) | [[code]](https://github.com/yhlleo/DeepSegmentor) | [[dataset]](https://github.com/yhlleo/RoadNet)

## 1.Dataset

![dataset](./figures/roadnet-dataset.jpg)

---------

We collected several typical urban areas of Ottawa, Canada from [Google Earth](http://earth.google.com). 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](https://pan.baidu.com/s/1l9RZvyYfLgTOx_k4LQRyhQ)（Password: h2zt）

 - [GoogleDrive](https://drive.google.com/open?id=1GDHy7uwgOswuCDC49OamlNkAxjaITPBI)

### 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

![](./figures/demo.jpg)

![](./figures/demo2.jpg)

## 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.