AUTOMATIC MOVING VEHICLE'S INFORMATION EXTRACTION FROM ONE-PASS WORLDVIEW-2 SATELLITE IMAGERY
Bibliographic record
Abstract
Abstract. There are several applications of vehicle information (position, speed, and direction). WorldView-2 satellite has three sensors: one Pan and two MS (MS-1: BGRN1, Pan, and MS-2:CYREN2). Because of a slight time gap in acquiring images from these sensors, the WorldView-2 images capture three different positions of the moving vehicles. This paper proposes a new technique to extract the vehicle information automatically by utilizing the small time gap in WorldView-2 sensors. A PCA-based technique has been developed to automatically detect moving vehicles from MS-1 and MS-2 images. The detected vehicles are used to limit the search space of the adaptive boosting (AdaBoost) algorithm in accurately determining the positions of vehicles in the images. Then, RPC sensor model of WorldView-2 has been used to determine vehicles' ground positions from their image positions to calculate speed and direction. The technique has been tested on a Worldview-2 image. A vehicle detection rate of over 95% has been achieved. The results of vehicles' speed calculations are reliable. This technique makes it feasible to use satellite images for traffic applications on an operational basis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".