Beyond visual-line-of-sight (BVLOS) drone operations for environmental and infrastructure monitoring: a case study in northwestern Canada
Bibliographic record
Abstract
Aerial drones typically operate over small geographic areas (<5 km2), yet environmental and infrastructure monitoring applications often require the collection of data over larger areas. Challenges to drone deployments in areas with people and frequent air traffic include aviation regulations that require missions to adhere to within-visual-line-of-sight (VLOS) conditions, thereby limiting mission extents. The performance and fuel consumption of longer drone missions could justify investment to advance future beyond-VLOS (BVLOS) data acquisitions. This work summarizes airspace deconfliction techniques that allowed testing of BVLOS capabilities in relatively busy airspace in northwestern Canada. Drone missions were conducted with a Griffon SeaHunter, capturing high-resolution imagery covering more than 550 km2 along 6200 km of flight lines, increasing conventional drone data coverage by two orders of magnitude. BVLOS hourly endurance was nearly double that of light aviation mapping aircraft, providing a suitable range for extended monitoring operations (1000–1200 km). Fuel consumption (L/100 km) also differed substantially; SeaHunter used 9%–16% of the fuel consumed by conventional mapping aircraft (84%–91% savings). Finally, we summarize lessons learned to further stimulate BVLOS adoption internationally. Opportunities will arise as BVLOS drones will increasingly be operated within a global context of transitions toward low-carbon emission economies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".