Road Extraction Based on Deep Learning Using Sdgsat-1 Nighttime Light Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it