Detecting information under and from shadow in panchromatic Ikonos images of the city of Sherbrooke
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
The presence of shadow is increasingly alarming on the images with very high spatial resolution mainly in urban area. After shadow detection, how to give an added value to detected shadow? Some kinds of exploitation are presented: the restitution of information under shadow and the deduction of object height from their shadow. The restitution of surfaces under shadow is based on the analysis of contextual and textural information between the shadow and its neighbouring surfaces not in sun side. Assuming that the same surface texture is independent of shadow. The height of objects is deduced knowing the length of their projected shadow on the ground, the position (azimuth and zenith) of the sun and the sensor at the time of acquisition. These exploitations of shadow were carried out on a panchromatic Ikonos image of Sherbrooke. Results are validated with land use map for surfaces under shadow, and measured height for the building height from shadow.
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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) | 0.000 | 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