Assessing image segmentation algorithms for sky identification in GNSS
Bibliographic record
Abstract
In order to improve the accuracy of user's position solution using Global Navigation Satellite System (GNSS) in urban canyons, it is important to know whether a satellite's signal is obstructed by surrounding buildings. This can be accomplished by using an upward-facing camera and segmenting the image into sky and non-sky. This paper evaluates the Otsu, Mean Shift, Graph cut and HMRF-EM-image image segmentation algorithms for this purpose. Since some algorithms provide two or more categories, segmentation is followed by k-means clustering techniques to yield only two categories; sky and non-sky. The algorithms are tested using images taken using an upward-facing camera at roughly the same locations in different weather conditions: cloudy and sunny. Result shows that, when images are appropriately adjusted, the Otsu method overcomes the three other algorithms in terms of the percentage of sky accurately segmented and is also more computationally efficient. Experiment was also perform in Calgary downtown to show the effect of segmentation on the GNSS accuracy. Results show that, when obstructed satellites are removed, the RMS of the residuals decreases significantly compare to when all satellites are used.
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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 | 0.002 | 0.005 |
| 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 | 0.000 |
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 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".