High-resolution satellite imagery applied to monitoring revegetation of oil-sands-exploration well pads
Bibliographic record
Abstract
ABSTRACT To achieve reclamation certification, oil-and-gas operations in Alberta, Canada are required to monitor the revegetation of idle well pads that no longer support operations. Currently, monitoring is completed by oblique, helicopter-collected photography and on-the-ground field surveys. Both monitoring strategies present safety and logistical challenges. To mitigate these challenges, a remote-sensing project was completed to develop and deploy a reproducible workflow using high-spatial-resolution satellite imagery to monitor revegetation progress on idle well pads. Seven well pads in the Aspen region of Alberta, Canada were selected for workflow development, using imagery from 2007, 2009, and 2011. Land-cover classes were derived from the satellite imagery using a training dataset, a series of vegetation indices derived from the satellite imagery, and regression tree classification programs, and were used to evaluate changes in vegetation cover over time. A refined version of this general workflow was then deployed across 39 well pads in the Firebag region of Alberta, Canada, using imagery from 2010 to 2016. In 2016, fieldwork was conducted across a subset of 16 well pads in the Firebag region, which facilitated a formal accuracy assessment of the land-cover classifications. This project demonstrated that high-spatial-resolution satellite imagery could be used to develop accurate land-cover classifications on these relatively small landscape features and that temporal land-cover classifications could be used to track revegetation through time. Overall, these results show the feasibility of remote-sensing–based workflows in monitoring revegetation on idle well pads.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".