Detection of temporal changes of the Omega House at the Athenian Agora
Bibliographic record
Abstract
This work presents the role of 3D visualization and analysis of monuments and archaeological sites in producing useful data regarding their preservation condition. The progress made in 3D digitization technologies, in combination with the development of new data processing algorithms, has enabled reliable and highly detailed digitization of the characteristics of different parts of the monuments. Due to both the effects of nature and human intervention, monuments and sites all over the world have undergone changes over time. The use of analog documentation data can help significantly towards this direction. In this work, we use as a case study a luxurious residential complex in the Athenian Agora, known as the Omega House. We use a retrospective 3D model, created with photographs taken in the late 60’s and early 70’s, in comparison with a 3D model made with contemporary digital photos, taken in 2017. All models are georeferenced. The old model is derived using analog terrestrial photographs and aerial photos taken by a blimp. The new one is created by terrestrial digital photographs in combination with images taken by an unmanned aerial vehicle, commonly known as a drone. The 3D models have been divided into smaller parts so that we can analyze them with greater accuracy separately, and then the whole models were compared as well. The Constructive Solid Geometry (CSG) modelling scheme is used and Boolean operations are applied to find the difference and intersection of the models. The comparison that is carried out in the current work elaborates on legacy data usefulness and their utility for monitoring the Omega House condition. The type of investigation proposed in this work proves that legacy data can be repurposed and can attain a new role through change detection techniques.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".