The Pléiades Glacier Observatory: high resolution digital elevation models and ortho-imagery to monitor glacier change
Bibliographic record
Abstract
Abstract. Spaceborne digital elevation models (DEMs) of glaciers are essential to describe their health, and their contribution to river runoff and to sea level rise. Publicly available DEMs derived from submeter satellite stereo-imagery were, up to now, mainly available in the polar regions and High Mountain Asia. Here, we present the Pléiades Glacier Observatory (PGO), a scientific programme acquiring Pléiades stereo pairs for 140 sites from Earth’s glacierized areas. The PGO product consists of DEMs at 2 m and 20 m ground sampling distance together with 0.5 m (panchromatic) and 2 m (multispectral) ortho-images. The DEMs are freely available to all registered users whereas ortho-images are available after signing a licence. PGO commenced in July 2016 in the North Hemisphere and February 2017 in the South Hemisphere. Each site is revisited every five years (cloud permitting), close to the end of the melt season, to measure glacier elevation change with an average uncertainty of 0.49 m (95 % confidence level, for a glacierized area of 1 km2), i.e. 0.1 m a-1. PGO samples over 20,000 km2 of glacierized terrain which represents about 3 % of the Earth’s glaciers area. This small sample, however, provides a first order estimate of the global glacier mass change and its decadal evolution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".