Comparative analysis of digital markers in photogrammetry and laser scanning for documentation of archeological immovable monuments
Bibliographic record
Abstract
Advances in the accessibility of surveying and geoscience tools and developments in computer science have led to significant growth and rapid developments in the efficiency and accuracy of heritage documentation practices from objects to entire buildings and complexes. Modern cultural heritage research relies heavily on producing accurate 3D models using digital documentation methods such as photogrammetry and laser scanning. Innovative approaches to 3D documentation through digital photogrammetry and laser scanning provide the opportunity to create digital twins of real cultural heritage monuments. For these digital twins to be relatable to the real world for quantitative analysis, they must be scaled and oriented in some way. The most common method for accurately scaling 3D models is through the use of markers. Markers ensure accurate spatial measurements by linking coordinates in the model to real-world coordinates. These markers help in image orientation, calibration, and 3D reconstruction. Despite their common use, currently, there is no set of designed markers that can be universally used across various modeling methods and software. The present investigation aims to identify different types of markers used for commercial and research purposes, comparing and contrasting their type, accuracy, and suitability for specific applications. Based on an evaluation of existing software solutions and indicators through laboratory tests, the qualities of markers will be analyzed and evaluated to facilitate precise 3D modeling and improve the reliability of data collected through photogrammetry and laser scanning. The effectiveness of markers will be analyzed through comparative studies investigating how different configurations and types of markers affect the overall accuracy and effectiveness of reconstructed 3D models of photographed objects. The findings are intended to provide insight into best practices for selecting and implementing markers in archaeological surveys, contributing to more accurate and reliable results from modeling, creating newly developed innovative markers, and allowing broader applicability for precise 3D modeling of photographed national cultural heritage sites.Acknowledgments: The authors would like to thank the Bulgarian National Science Fund for funding the research under the project "An integral approach in creating digital twins of archeological immovable monuments using innovative technologies", contract КP-06-Н82/1 - 06.12.2024.
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.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".