Use of Advanced Processing Techniques of High Density LiDAR in Place of Survey for Cost and Schedule Reductions on Early Phase Pipeline Projects: Capital Project Results
Bibliographic record
Abstract
In the early phases of a pipeline project, the lack of data available for routing results in multiple routes being considered and interactions with land owners with only a general route. As a consequence, multiple survey programs are undertaken only to collect data that often is not being used, or site revisits are required to collect additional survey data. Repeated visits by survey crews to the field results in considerable cost and schedule delay to the project. A project was undertaken to reduce these costs and schedule impacts by replacing the need for survey work during FEED by utilizing remote sensing data and Artificial Intelligence (AI). This paper outlines the methods for collection of High Density (HD) LiDAR and high resolution imagery and then using AI to automatically identify all above ground features. This technique has proven to provide data of sufficient accuracy and completeness to conduct Pre-FEED and FEED level routing and engineering of a pipeline project. When survey is required, it is only to collect below ground features and to collect or verify data in very specific detail. Additionally, when required for legal or engineering reasons, survey work can be conducted more efficiently by providing the crews with a field database of the features already captured and means for updating the database with the data they collect or verify.
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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 | 0.000 | 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 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".