UBR-Net: Road Extraction from High-Resolution Remote Sensing Imagery Using Multi-Scale Attention and Cross-Residual Encoding
Bibliographic record
Abstract
Extracting road features from high-resolution remote sensing imagery is crucial for urban planning, navigation systems. However, this task is challenged by factors such as occlusions from buildings, interruptions in road continuity, and variations in road width and appearance. These issues often lead to segmentation discontinuities and misclassifications. This paper presents the Urban Road Extraction Network (UBR-Net), an architecture that enhances the DeepLabv3+ model to address these challenges. The key contributions lie in the specific design and integration of several modules. We introduce Cross-Residual Encoding blocks designed to preserve fine-grained details and mitigate the gradient vanishing problem, thereby improving road continuity. Additionally, UBR-Net incorporates a Multiscale Context Features Extraction (MCFE) module, enhanced with an Improved Self-Attention Block (ISAB), to capture rich, hierarchical feature representations with a focus on long-range dependencies. A Channel Spatial Attention Module (CSAM) is also integrated to refine the feature extraction process by focusing on critical channels and spatial regions. Evaluations on public datasets, including DeepGlobe and Massachusetts, show that UBR-Net reduces extraction errors from occlusions, achieving a 75.18% F1 score and a 57.22% Intersection over Union (IoU), surpassing existing methods. These results highlight UBR-Net’s effectiveness for urban analysis and its potential for more precise and efficient urban planning.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".