Microdrones in field-based structural geology: a photogrammetry and fracture quantification case study from the North Mountain Basalt, Nova Scotia, Canada
Bibliographic record
Abstract
Drone use in geoscience research and teaching is becoming widespread, with diverse applications documented. Many studies favour consumer-level drones, however, recent developments in so-called “microdrones” (takeoff weight < 250 g) necessitate further investigation to determine possible benefits, limitations, and future developments. Microdrone deployment is often advantageous in numerous jurisdictions due to fewer regulations, lower cost, and simple transportation. In this study, we deployed a DJI Mini 2 microdrone to study the ca. 201 Ma North Mountain Basalt (NMB) exposed in coastal outcrops along the Bay of Fundy, Nova Scotia, Canada. We report benefits of the microdrone as a field aid with three related approaches: (1) general site location and characterisation, (2) drone-based photogrammetry using ArcGIS Drone2Map, and (3) quantitative fracture mapping using FracPaQ. Application of these methods showed that microdrone-acquired imagery from the NMB exposures provides a valuable resource for interpretation post-fieldwork. The microdrone-derived data show two near-perpendicular fracture sets in the NMB: ∼NNE–SSW and ∼ESE–WNW, with some variation along the coastline. Overall, we determined that microdrones offer field-based geoscientists a valuable tool due to quick deployment, a simple image capture process and relatively straightforward data processing, and thus predict that this approach to enhancing fieldwork will continue to advance.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".