Investigating archaeological remains at Stracciacappe, Rome: comparing traditional sources with UAV-based multispectral, thermal and microtopographic analysis
Bibliographic record
Abstract
This study investigates the applicability of drone technology in examining Stracciacappe, a minor archaeological site through low-altitude aerial photography. Using multispectral and thermal sensors mounted on DJI Phantom Multispectral and DJI Mavic Enterprise Advanced drones, several flight missions were conducted in November 2020, May 2021, and April 2022. The effectiveness of analyzing multispectral and thermal raw images was limited by the area’s irregular vegetation, which hindered the clear detection of archaeological anomalies. However, microtopographic analysis employing various visualization techniques revealed significant traces, aligning with the site’s description found in numerous documentary sources. This includes the identification of two distinct areas within the castrum: the elevated cassarum and the burgus, along with potential traces of defensive structures within these areas. Drone analysis delineated a cassarum comprising a tower, palatium, and defensive walls, while the burgus seemed devoid of buildings, supporting the notion of a village primarily constructed with perishable materials. Thus, the study highlights the importance of using diverse sensor-based drone analyses to enhance archaeological investigations at minor sites.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".