VNet: An End-to-End Fully Convolutional Neural Network for Road Extraction From High-Resolution Remote Sensing Data
Bibliographic record
Abstract
One of the most important tasks in the advanced transportation systems is road extraction. Extracting road region from high-resolution remote sensing imagery is challenging due to complicated background such as buildings, trees shadows, pedestrians and vehicles and rural road networks that have heterogeneous forms with low interclass and high intraclass differences. Recently, deep learning-based techniques have presented a notable enhancement in the image segmentation results, however, most of them still cannot preserve boundary information and obtain high-resolution road segmentation map when processing the remote sensing imagery. In the present study, we introduce a new deep learning-based convolutional network called VNet model to produce a high-resolution road segmentation map. Moreover, a new dual loss function called cross-entropy-dice-loss (CEDL) is defined that synthesize cross-entropy (CE) and dice loss (DL) and consider both local information (CE) and global information (DL) to decrease the class imbalance influence and improve the road extraction results. The proposed VNet+CEDL model is implemented on two various road datasets called Massachusetts and Ottawa datasets. The suggested VNet+CEDL approach achieved an average F1 accuracy of 90.64% for Massachusetts dataset and 92.41% for Ottawa dataset. When compared to other state-of-the-art deep learning-based frameworks like FCN, Segnet and Unet, the proposed approach could improve the results to 1.09%, 2.45% and 0.39%, for Massachusetts dataset and 7.21%, 1.86% and 2.68%, for Ottawa dataset. Also, we compared the proposed method with the state-of-the-art road extraction techniques, and the results proved that the proposed technique outperformed other deep learning-based techniques in road extraction.
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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) | 0.002 | 0.001 |
| 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) | 0.003 | 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".