A pixel- and object-based image analysis framework for automatic well site extraction at regional scales using Landsat data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Development associated with oil and gas exploration has expanded rapidly in Alberta and Northwest Territories, Canada. Such explorations result in landscape disturbances including forest cuts, seismic lines, well and waste sites. This paper describes a novel methodology for automatic extraction of well sites from Landsat-5 TM imagery. The method combines pixel-based and object-based image analyses and contains three major steps: geometric enhancement, segmentation, and well site extraction. For accuracy assessment, a small part of the image was used and the results were compared against visual counting of well sites visible in the pan-sharpened image of Landsat-8 of the same area. Results show correctness, completeness and quality factors of 87.3%, 96.2%, and 83.7%, respectively.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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