OBJECT-BASED MOVING VEHICLE EXTRACTION FROM WORLDVIEW2 IMAGERY
Bibliographic record
Abstract
Moving vehicle detection is very important for transportation management and traffic monitoring. Due to the submeter spatial resolution of very high resolution (VHR) imagery, vehicles can be identified from this type of imagery. Furthermore, because of the slight time difference between image acquisition of onboard sensors, (i.e. Pan and MS sensors) in VHR satellite such as Quickbird and GeoEye-1, a moving vehicle is observed, by the satellite, at two different locations. Consequently, moving vehicles can be distinguished from the stationary ones by applying a proper change detection algorithm. WorldView2 possess three sensors, i.e. a Pan and two MS sensors (MS1 and MS2). Therefore, a moving vehicle is observed at three different locations. This feature together with the new spectral bands of WV2 adds opportunity to improve moving vehicle detection and extraction. This paper, utilizing an object-based framework, compares the automatic moving vehicle extraction by using the three pairs of WV2 sensors (i.e. Pan-MS1, Pan-Ms2 and MS1-MS2). The results show that of three image pairs, the MS1-MS2 is the best choice for moving vehicle extraction because of the larger time lag between MS1 and MS2 than between the Pan and MS1 or MS2.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".