Towards drone-based magnetometer measurements for archaeological prospection in challenging terrain
Bibliographic record
Abstract
While airborne magnetometry has been used for geological surveys for decades, magnetic surveys for archaeological prospection are almost exclusively ground-based, as the detection of archaeological features requires higher spatial resolution and close proximity between sensor and object. However, the recent development of drones and magnetic sensors allows for low-altitude drone-based surveys, which are an interesting alternative for magnetic prospection of challenging areas, where vegetation, difficult terrain, access restrictions, or safety concerns hamper ground-based surveys. In this paper, we present test measurements in challenging areas in Germany and Switzerland, which demonstrate the potential as well as technical and practical concerns of drone-based magnetometry for archaeological prospection. We used a miniature total-field magnetometer, which was tethered to an octocopter drone. Although it is preferable to fly the sensor close to the ground where anomalies show the highest values, we could also detect magnetic anomalies in altitudes up to few metres above ground. Flights at different altitudes show the decay and widening of the anomalies with height. Drone-based magnetic measurements in rough and vegetated terrain require careful flight planning based on a high-resolution surface model. Further development is needed to improve positioning accuracy of the tethered magnetometer and to improve heading error correction.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".