The Use of 3D Photogrammetry in the Analysis, Visualization, and Dissemination of the Indigenous Archaeological Heritage of the Greater Antilles
Bibliographic record
Abstract
Abstract The development of digital technologies and the use of advanced photogrammetry programs for modeling archaeological excavations and sites have opened new possibilities for spatial analysis in archaeology and the reconstruction of archaeological contexts. In addition, these tools allow us to visually preserve the features of archaeological sites for future use and facilitate the dissemination of archaeological heritage to local communities and the general public. This paper summarizes 3D photographic visualization of three cave art sites (Los Cayucos and Cueva No. 1 in Punta del Este, Cuba, and José María Cave in the Dominican Republic) and two burial spaces (Canímar Abajo and Playa del Mango, Cuba) using photogrammetry software. The application of these novel methods at the cave art sites allowed us to visualize faint pictographs that were invisible to the naked eye, to better define the shapes of petroglyphs and to reconstruct the position of lost/removed panels. At the burial sites, 3D modeling allowed us to register the archaeological context with greater precision. The use of 3D modeling will improve spatial analysis and data safeguarding in Cuban archaeology. Moreover, 3D movies are an effective way to disseminate knowledge and connect local communities with their cultural heritage, while reducing the impact of public visits to remote or endangered 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".