Archaeological Aerial Thermography in Theory and Practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.008 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it