ASSESSMENT OF RADARSAT-2 HR STEREO DATA OVER CANADIAN NORTHERN AND ARCTIC STUDY SITES
Bibliographic record
Abstract
Abstract. Digital surface models (DSMs) extracted from high-resolution Radarsat-2 (R2) stereo images using a new hybrid radargrammetric modeling developed at the Canada Centre for Remote Sensing are evaluated over two Canadian northern and arctic study sites. Because the new hybrid model uses the full metadata of R2, it does not require any ground control point. The first study site in the north of Quebec is used for the scientific validation where accurate checked data (dGPS, lidas) is available. The second study site in the Arctic (steep relief and glaciated surfaces) is challenging for the operational evaluation of topographic mapping capabilities of R2. For the first study site, the bias and elevation linear errors with 68 percent confidence level (LE68) of R2 stero-extracted DSM compared to lidar data were computed over bare surfaces: LE90 of 3.9 m and no bias were achieved. For the second study site the comparison was performed between the R2 DEM and ICESat data. A negative 18-m bias was computed and certainly results suggests a bias in the stereo-model of R2 and thus in the metadata used in the model computation because there is few temporal variation in the data acquisition (R2 and ICESat)/ LE68 of 28 m was obtained. However, the differential melting and thinning depending of the glaciers elevations and planimetric surging of glacier tongues with less accumulation of debris and moraines, a lower LE68 of around 20 m could be expected. In addition to evaluate the potential of R2 over ice bodies, which generally have low slope relief and because the errors are strongly correlated with slopes, other statistical results of elevation differences were also computed: LE68 of 15 m was obtained over ice fields with 0–5° slopes while a little more than 20-m over less than 30° slopes was achieved.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".