Analyse comparative de l'information topographique obtenue à partir des modèles numériques d'altitude de différentes sources
Bibliographic record
Abstract
There is always a margin of error concerning elevation data, which are considered one of the most important types of topographic information obtained from Digital Elevation Models (DEMs). Today, DEMs are the most useful data source for geospatial analysis and are highly requested. To achieve accuracy of DEMs, new techniques came out. Airborne Laser Scanning (LIDAR) and the Interferometric Synthetic Aperture Radar (IFSAR) are among the latest methods used in remote sensing to produce DEMs.The two techniques do not have the same advantages and disadvantages. Taking into account the morphology of terrain and factors related to vegetation, we have proceeded to various comparisons of topographic information obtained from ICES at elevation data, Canadian Digital Elevation Data (CDED), and SRTM models. We used more than 8 million points distributed in eight study areas throughout Canada. A comparison between CDED and SRTM indicated an RMSE of 11, 9 m. Vertical accuracy was found to be surface slope dependent. Comparisons made with ICESat LIDAR elevation points on SRTM and CDED models confirmed the important influence of slope on topographic information. ICES at produces excellent results in plane regions for slopes ? 5À (RMSE of 1, 5 m found in Manitoba). While comparing ICESat/SRTM with ICESat/DNEC, we observed that ICESat/SRTM presented the fewest errors. Errors between CDED and SRTM models are concentrated around a north-south axis, particularly in northern directions. ICESat/SRTM confirmed the concentration of errors in the northern directions. Comparisons showed that conifers are the species which had a major influence on the differences between the two models with an RMSE of 6,7 m.The density of vegetation does not have a significant impact on the topographic information between SRTM and CDED.The highest trees have more influence on the topographic information, with an RMSE above 5 m. However, vegetation does not influence ICES at and SRTM in the same manner. From the existing relation between contour interval and RMSE, we derived some topographical scale ranges which enable mapping with SRTM data at a scale better than 1: 250 000. For the most part, the observed RMSE between CDED and SRTM fulfills the 16 m RMSE specification for the SRTM mission. Despite the low distribution of ICESat elevation points, the importance of this satellite cannot be overemphasized: ICES at points remain a power validation tool in satellite altimetry.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".