Archaeological Aerial Thermography in Theory and Practice
Bibliographic record
Abstract
ABSTRACT While a long history of experimental data shows that aerial thermal images can reveal a wide range of both surface and subsurface archaeological features, technological hurdles have largely prevented more widespread use of this promising prospecting method. However, recent advances in the sophistication of thermal cameras, the reliability of commercial drones, and the growing power of photogrammetric software packages are revolutionizing archaeologists' ability to collect, process, and analyze aerial thermal imagery. This paper provides an overview of the theory behind aerial thermography in archaeology, as well as a discussion of an emerging set of methods developed by the authors for undertaking successful surveys. Summarizing investigations at archaeological sites in North America, the Mediterranean, and the Near East, our results illustrate some contexts in which aerial thermography is very effective, as well as cases in which ground cover, soil composition, or the depth and character of archaeological features present challenges. In addition, we highlight novel approaches for filtering out noise caused by vegetation, as well as methods for improving feature visibility using radiometric thermal imagery.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".