Casdenet: Cascade Automatic Road Detection Network Based on Dynamic Snake Convolution and Edge Branch
Bibliographic record
Abstract
Automated road detection from the high-resolution remote sensing images (RSI) is always a hot topic. Particularly, the accurate and continuous road detection in RSI is challenging due to the tree and shadow shading and unsmooth road edges further hindering the accuracy of the road extraction. Considering that the dynamic snake convolution (DSConv) is able to capture the distinctive characteristics of tubular objects such as roads, and edge information extracted from road edge branch can enhance the smoothness of road edges, we propose a cascade automatic road detection method based on DSConv and edge branch named CasDeNet. Specifically, the DSConv is introduced as the foundational module for low-level feature extraction of CasDeNet, aiming to capture the intricate shapes of roads. Road edge information from the edge branch is incorporated to ensure the smoothness of the road edges. Experiments are conducted on the Ottawa dataset. The results show that the proposed CasDeNet can extract more coherent and accurate roads compared to other state-of-the-art (SOTA) methods and achieve the best results.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".