Operational use of RADARSAT-1 fine stereoscopy integrated with Landsat-5 thematic mapper data for cartographic application in the Brazilian Amazon
Bibliographic record
Abstract
The feasibility of topographic mapping through orbital remote sensing was investigated for the Brazilian Amazon. The study area is in a region of low topographic terrain within the Tapajós National Forest. Two kinds of radargrammetric digital elevation models (DEMs), one based solely on satellite ancillary data and one calibrated with ground control points (GCPs), were produced based on a fine RADARSAT-1 stereopair (F2/F5) and evaluated regarding accurate field planialtimetric measurements. The geometric modeling for the DEM extractions was based on the RADARSAT-1 specific model from OESE software (PCI Geomatics Inc.). The planimetric features were extracted from integrated fine and Landsat thematic mapper (TM) products. Precise topographic field information from a differential global positioning system (DGPS) was used as GCPs for the modeling of the DEMs and for the orthorectification of the synthetic aperture radar (SAR) and optical data and as independent check points (ICPs) for the calculation of planialtimetric accuracies of the products. The investigation has shown that the accuracy of the topographic map met the requirements for a 1 : 100 000 scale map (class A) as requested by the Brazilian Standard for Cartographic Accuracy. The approach is a realistic alternative for topographic mapping at a semi-detailed scale in similar environments of the Amazon, where terrain information is seldom available or is of low quality.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".