Road Extraction Based on Deep Learning Using Sdgsat-1 Nighttime Light Data
Bibliographic record
Abstract
Previous road extraction research based on remote sensing data mainly used traditional optical remote sensing imagery acquired during the daytime, but some roads on the images were affected by problems such as building shadows and similar spectral features of road with other materials, which reduced the accuracy of road extraction. The Glimmer Imager (GI) equipped on the Sustainable Development Science Satellite 1 (SDGSAT-1) provides nighttime light (NTL) images with relatively high spatial resolution (RGB: 40m; PAN: 10m), which show detail of road networks. In order to explore the potential of the NTL data obtained from SDGSAT-1 on road extraction, a deep learning framework combining ResUnet and Dynamic Snake Convolution (DSC) module was proposed in this study. The experimental results demonstrated the effectiveness of SDGSAT-1 NTL on road extraction tasks and the extracted roads can be used for road dataset updating.
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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 | 0.002 |
| 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 | 0.002 |
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".