Modernization of Canadian Digital Topographic Data
Bibliographic record
Abstract
Canadians have been without a current Canada-wide digital terrain model since the prior product was not updated since 2011.Natural Resources Canada's elevation data team has been active with the acquisition of high-resolution data, leveraging lidar data and optical satellite imagery.However, this initiative will take several more years before achieving complete coverage across all of Canada's land mass.Satellite derived elevation models are numerous, providing global coverage.These models are surface models, containing vegetation and man-made infrastructure.Many applications require a terrain model, which represents 'bare-earth' elevation.In this project, we started with a global surface model, i.e.Copernicus GLO-30, and performed numerous spatial operations to extract a terrain model from it, through the inclusion of auxiliary datasets such as settled areas and forest heights, to create a modified Copernicus terrain model.Data fusion was then performed to include down sampled lidar-derived highresolution terrain data where available, as this data is of better vertical accuracy.The result is a new, modern, and supported 30m resolution raster, which will be updated as new lidar data are released.The vertical accuracy of the 30m MRDTM is compared to the high-resolution data, before it's fused into the MRDTM, and to a variety of RTK control points in vegetated and non-vegetated regions.Results indicate the MRDTM is a significant improvement over the previously available national elevation datasets.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".