A new Bayesian ensemble of trees classifier for identifying multi-class\n labels in satellite images
Why this work is in the frame
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Bibliographic record
Abstract
Classification of satellite images is a key component of many remote sensing\napplications. One of the most important products of a raw satellite image is\nthe classified map which labels the image pixels into meaningful classes.\nThough several parametric and non-parametric classifiers have been developed\nthus far, accurate labeling of the pixels still remains a challenge. In this\npaper, we propose a new reliable multiclass-classifier for identifying class\nlabels of a satellite image in remote sensing applications. The proposed\nmulticlass-classifier is a generalization of a binary classifier based on the\nflexible ensemble of regression trees model called Bayesian Additive Regression\nTrees (BART). We used three small areas from the LANDSAT 5 TM image, acquired\non August 15, 2009 (path/row: 08/29, L1T product, UTM map projection) over\nKings County, Nova Scotia, Canada to classify the land-use. Several prediction\naccuracy and uncertainty measures have been used to compare the reliability of\nthe proposed classifier with the state-of-the-art classifiers in remote\nsensing.\n
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it