Brief Review — ResUNet: Road Extraction by Deep Residual U-Net

ResUNet, U-Net With Residual Paths

Sik-Ho Tsang
2 min readApr 2, 2023

Road Extraction by Deep Residual U-Net,
ResUNet, by Beihang University,
2018 IEEE Geoscience and Remote Sensing Letters, Over 1500 Citations (

@ Medium)

Semantic Segmentation
20142021 [PVT, PVTv1] [SETR] [Trans10K-v2, Trans2Seg] [Copy-Paste] 2022 [PVTv2] [YOLACT++]

  • A semantic segmentation neural network, ResUNet, is proposed, which combines the strengths of residual learning and U-Net, is proposed for road area extraction.


  1. ResUNet
  2. Results

1. ResUNet

ResUNet Overall Architecture
  • In this paper, residual block or short skip connection (blue dahed lines) is applied instead of just using plain convolutions as in U-Net.
  • 7-level architecture of deep ResUnet is used.
  • MSE is used as loss function:
  • where N is the number of the training samples.
  • Overlap strategy, as in U-Net, is also used here.

2. Results

SOTA Comparisons on Massachusetts Road Dataset
  • The break-even point is defined as the point on the relaxed precision-recall curve, where its precision value equals its recall value.

ResUNet performs better than all other three approaches in terms of relaxed precision and recall.

Example results on the test set of Massachusetts roads data set.
  • (a) Input image. (b) Ground truth. (c) Saito et al. [5]. (d) U-Net. (e) Proposed ResUNet.

Even the runway has very similar features to a highway, the proposed method can successfully segment side road from the runway.



Sik-Ho Tsang

PhD, Researcher. I share what I learn. :) Linktree: for Twitter, LinkedIn, etc.