Application of Remote Sensing Imagery and Ancillary Products to Improve Safety and Logistical Efficiency of Arctic Operations
Bibliographic record
Abstract
Remote Sensing Imagery and the derived ancillary products improved the efficiency and safety of upstream oil and gas operations on the North Slope of Alaska. These Arctic regions are remote, very difficult to access in general and sometimes only seasonably accessible. Our prudent and responsible Arctic Operations require regional-reconnaissance exploration, diligent monitoring of environment such as current state of vegetation, temporal changes of terrain, water drainage system and lakes. Finally, we also need very detailed logistical-planning of field operations. Remote sensing imagery and its derived ancillary products demonstrably improved all these aspects of our Arctic Operations. For Arctic Operations, remote sensing data consisted of optical satellite and aerial imagery at various spectral and spatial resolutions, high resolution LIDAR data for digital elevation and digital surface models and synthetic aperture radar imagery (SAR). A combination of in-house and commercial software was used to ingest and process these data. The optical imagery was processed and enhanced using various spectral combinations and high pass filtering to generate the highest possible spatial-resolution for each sensor. Classic neural networks analysis was used to classify the optical imagery for vegetation. The SAR imagery was calibrated (for all polarizations) and geometrically corrected to remove layover effects. The processed optical and SAR imagery, LIDAR and ancillary products were co-registered and imported into a GIS system for final analysis and applications. The optical imagery provided information about surface feature such as lake outlines, general drainage, active channels in Colville River, general lake ice conditions, classification of vegetation types etc. The LIDAR data were used to generate slope maps (for arctic vehicles), general topographic conditions and field operations. The SAR imagery was used to monitor surface conditions when optical imagery was not available during the Arctic night conditions. SAR imagery was also used to calculate the ice thickness proxy maps for eventual field operations. All of these products contributed directly to our environmental baseline studies, improved our field operation efficiency and general safety of our Arctic Operations. For a practicing engineer (individual or team) The remote sensing data and derived products for Arctic Operations were made available via GIS system. This allowed easy integration with other data layers as well as a common background for all different disciplines to monitor progress and to contribute their learnings and ideas to the entire team.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".