Change Detection Analysis Using Drone-Based Photogrammetry for Long-Term Archaeological Site Erosion Monitoring
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
This paper presents a rapid, cost-effective, and non-specialist approach to long-term archaeological site monitoring that is repeatable and affordable. Advances in unmanned aerial vehicle (UAV)-based photogrammetry allow for the creation of multi-temporal three-dimensional (3D) models that permit accurate and in-depth analysis and quantification of landscape change through time. This paper presents a case study of aerial-based photogrammetric datasets using UAVs (i.e., drones) to conduct change detection analysis for monitoring continued erosion threatening an Indigenous buffalo jump in Alberta, Canada. The results demonstrate which areas of the site experienced the most significant change over a period of four years. By bridging gaps between traditional field survey and satellite-scale observations of impacts to large archaeological site complexes, UAV monitoring programs may become increasingly important as anthropogenic climate change continues to threaten heritage sites in Canada.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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