Heterogeneous dual-decoder network for road extraction in remote sensing images
Bibliographic record
Abstract
Accurate road extraction from remote sensing images is crucial for autonomous driving, urban planning, and route planning. However, existing methods struggle to address the challenges of scale variation, occlusion, and blurred boundaries. To tackle these challenges, this paper proposes a heterogeneous dual-decoder network (HDDNet), which aims to simultaneously solve the multiple problems in remote sensing road extraction by designing two decoders with complementary functions. Specifically, the main decoder incorporates the Dynamic Snake Grouping Dilation (DSGD) module, which combines road morphological features with a grouped multi-scale receptive field to enhance the capture of narrow and multi-scale road features. The auxiliary decoder integrates the Multi-directional Connectivity and Boundary Enhancement (MCBE) module, which jointly optimizes road connectivity and boundary refinement by leveraging directional consistency between the road body and edges. Finally, the Dual Attention Feature Fusion (DAFF) module is introduced to interactively learn and fuse the output features of the main decoder and the auxiliary decoder in both spatial and channel dimensions, which improves the accuracy and robustness of feature representations. We conducted systematic experiments on three representative public datasets: DeepGlobe, Ottawa, and CHN6-CUG. The results demonstrate that the proposed method significantly outperforms current mainstream approaches in the road extraction task, achieving Intersection over Union (IoU) scores of 71.36%, 91.85%, and 67.27%, respectively, which strongly validates the performance and robustness of HDDNet across diverse road scenarios.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".