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September 5, 2025Scientific ReportsOpen Access

Heterogeneous dual-decoder network for road extraction in remote sensing images

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Authors

SQShenming QuGLGang LiuXZXiangnan Zhang

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Overview

This innovative approach enhances road extraction accuracy in remote sensing images, suggesting improved urban planning applications.

Key Points

  • The proposed heterogeneous dual-decoder network improves road extraction accuracy significantly.
  • Achieving IoU scores of 71.36%, 91.85%, and 67.27% across three datasets demonstrates effectiveness.
  • The method incorporates unique modules to enhance multi-scale feature capture and boundary refinement.
  • Results indicate robustness and superior performance compared to mainstream road extraction methods.

Cite This Study

Qu et al. (2025) studied this question.

synapsesocial.com/papers/68bb3a352b87ece8dc954e01https://doi.org/10.1038/s41598-025-17445-9
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1DEDU-Net: Dual-Encoder-Decoder-U-Net for road extraction from high-resolution remote sensing images2024 · 3 citations
  2. 2DVDNet: a dual-view decoding network for road extraction2026
  3. 3An Enhanced Feature Extraction and Multi-Branch Occlusion Discrimination Network for Road Detection from Satellite Imagery2025
  4. 4A remote sensing image road extraction algorithm assisted by multidimensional features with oriented coordinate attention2026
  5. 5A Deep Neural Network for Road Extraction with the Capability to Remove Foreign Objects with Similar Spectra2024