Documenting drone remote sensing: a reality-based modelling approach for applications in cultural heritage and archaeology
Bibliographic record
Abstract
This paper addresses the design of open, reproducible, and transferable workflows for remote sensing data processing in archaeology, the specific case being drone (UAS) remote sensing data. In the context of increased application of remote sensing, stimulated by both recent technological developments, as well as threats to the buried archaeological record by developments such as agricultural intensification and climate change, it is important to allow the archaeological community to really benefit from the multitude of remote sensing applications and their diverging modalities. With the ever-increasing remote sensing datasets spread throughout the field of cultural heritage and archaeology, it has become even more important to be able to clearly communicate the process from data capture to the eventual visualised data model and further archiving and dissemination. This is crucial to scientific transparency required for the assessment of data quality, for example to allow for evaluating interpretations, comparative research, and replication studies. The ultimate goal is to permit data publication adhering to FAIR (Findable, Accessible, Interoperable, Reusable) principles, for which a good metadata documentation is a cornerstone.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".