Classification d'ortho-photographies numérisées pour une cartographie à grande échelle de la végétation terrestre
Bibliographic record
Abstract
AbstractThe objective of the study is to produce a cartography of coastal vegetation based on ortho-images, using digitized infrared and color aerial photographs (of 1993), a Digital Elevation Model, and unsupervised classification processes. The ortho-rectification allows the combination of the infrared and color images with a geographic information database. Estimates from a cartographic validation index, indicate that, with or without taking in account the infrared image, the results of the classification are quite equivalent, with an averaged 70% accuracy. However, the identification of ligneous vegetation, based on the classification of main land-cover types applied to the color ortho-image, is 85% accurate. These results illustrate the significance of applied automated classification of ortho-images to the large-scale monitoring of dynamic vegetation processes.Cette étude préliminaire a pour objectif de réaliser une cartographie de la végétation littorale. Elle s'appuie sur la production d'ortho-photographies numérisées à partir de clichés anciens (1993) et d'un modèle numérique de terrain, ainsi que sur la mise en œuvre de procédures de classification non dirigée. L'ortho-rectification permet de combiner les deux émulsions disponibles (infra-rouge et couleur) avec le contenu d'une base d'information géographique. Les résultats des classifications, estimés par l'indice de validité cartographique, sont globalement équivalents, avec ou sans prise en compte de l'infra-rouge, et indiquent des performances moyennes (de l'ordre de 70 %). Toutefois, la détection des formations ligneuses, par une méthode de classification par grand type de milieu appliquée à l'image couleur est acceptable (validité de 85 %). Ce résultat préliminaire témoigne de l'intérêt des classifications automatiques d'ortho-photographies numérisées, pour le suivi à grande échelle des processus dynamiques tels que l'embroussaillement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".