Remote Predictive Mapping 3. Optical Remote Sensing – A Review for Remote Predictive Geological Mapping in Northern Canada
Bibliographic record
Abstract
Optical remotely sensed data have broad application for geological mapping in Canada’s North. Diverse remote sensors and digital image processing techniques have specific mapping functions, as demonstrated by numerous examples and associated interpretations. Moderate resolution optical sensors are useful for discriminating rock types, whereas sensors that offer increased spectral resolution (i.e. hyperspectral sensors) allow the geologist to identify certain rock types (mainly different types of carbonates, Fe-bearing rocks, sulphates and hydroxyl-(clay-) bearing rocks) as opposed to merely discriminating between them. Increased spatial resolution and the ability to visualize the earth’s surface in stereo are now offered by a host of optical sensors. However, the usefulness of optical remote sensing for geological mapping is highly dependent on the geologic, surficial and biophysical environment, and bedrock predictive mapping is most successful in areas not obscured by thick drift and vegetation/lichen cover, which is typical of environments proximal to coasts. In general, predictive mapping of surficial materials has fewer restrictions. Optical imagery can be enhanced in a variety of ways and fused with other geo-science datasets to produce imagery that can be visually interpreted in a GIS environment. Computer processing techniques are useful for undertaking more quantitative analyses of imagery for mapping bedrock, surficial materials and geomorphic or glacial features. SOMMAIRE Les donnees recueillies par teledetection optique offrent beaucoup de possibilites pour la cartographie geologique des regions nordiques canadiennes. La diversite des telecapteurs et des techniques de traitement numerique des donnees permet la definition de fonctions de cartographie specifique, tel que l’illustre de nombreux exemples et interpretations associees. Des capteurs optiques de moyenne resolution sont utiles pour differencier les types de roche, alors que les capteurs a plus fines resolutions (les capteurs hyperspectraux, par ex.) permettent au geologue de subdiviser certains types de roches (principalement differents types de carbonates, roches ferrugineuses, roches a sulfates et a hydroxyle (argile). Une meilleure resolution spatiale et la fonction de vision stereoscopique sont maintenant offertes sur une gamme de capteurs optiques. Cela dit, l’utilite de la teledetection optique pour la cartographie geologique est fortement tributaire des conditions de la geologie de surface et de son environnement biophysique, le potentiel predictif de la telecartographie etant maximal pour les regions exemptes d’une couverture epaisse de depots glaciaires ou d’une couverture vegetale/lichen caracteristique typique des environnements longeant les cotes. Divers procedes permettent de rehausser l’imagerie optique et de realiser des fusions avec d’autres jeux de donnees geoscientifiques et de produire une imagerie visuellement inter-pretable en environnement de SIG. Les techniques de traitement de donnees par ordinateur sont utiles pour d’autres types d’analyse quantitative d’imagerie pour la cartographie des materiaux de couverture du socle et pour repertorier des formes glaciaires et geomorphologiques.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".