Papers › D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution...
D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction
Lichen Zhou, Chuang Zhang, Ming Wu
Road extraction is a fundamental task in the field of remote sensing which has been a hot research topic in the past decade. In this paper, we propose a semantic segmentation neural network, named D-LinkNet, which adopts encoderdecoder structure, dilated convolution and pretrained encoder for road extraction task. The network is built with LinkNet architecture and has dilated convolution layers in its center part. Linknet architecture is efficient in computation and memory. Dilation convolution is a powerful tool that can enlarge the receptive field of feature points without reducing the resolution of the feature maps. In the CVPR DeepGlobe 2018 Road Extraction Challenge, our best IoU scores on the validation set and the test set are 0.6466 and 0.6342 respectively.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Road Segmentation | DeepGlobe | D-LinkNet | IoU | 0.6412 | #2 of 3 | Archive leaderboard | report |
| Semantic Segmentation | BJRoad | D-LinkNet | IoU | 57.96 | #7 of 11 | Archive leaderboard | report |
| Semantic Segmentation | Porto | D-LinkNet | IoU | 70.20 | #6 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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