Very close nadiral images: a proposal for quick digging survey
Bibliographic record
Abstract
The main goal of our activity has been the testing of a system for quick and non invasive images acquisition and their suitable processings aimed to obtain 2Do 3D models for archaeological diggings documentation. This purpose has implied the analysis and the selection of a simple and efficient data processing system for the generation of metric products such as digital elevation models and orthophotos, featured by an high level of detail regarding the excavated areas. The acquiring system has to be quick to meet the excavation requirements and the processing system has to be featured by good quality in terms of accuracy and information richness to ensure suitable final products. The objective of acquiring very close nadiral images is made up by a mobile framework easily assembled directly on site. The production of orthophotos was carried out starting from the generation and comparison of three different DEMs, originated from different source data and defined by dissimilar accuracy; the evaluation of DEMs is aimed to single out which kind of them, sufficiently meet archaeological excavation requirements, or better, which level of automation fulfil the overall quality reached by more time-spending and traditional methods. The first DEM (a) has been obtained by a traditional method, i.e. a non-gridded DTM, acquired by topographical method directly on site (by a total station), which has been integrated by breaklines and scattered points measured by stereo-plotting. The second DEM (b) has been derived from a DTM automatically obtained by a robotic total station scanning, whereas the last, DEM (c), was produced by image matching solely. The evaluation of the accuracy of the three different DEMs and related ortho projections let us testing some different kinds of elevation models generation and their suitability for antropic nature objects: the Inverse Distance Weighting algorithm processed with dense break-lines; mesh generation, based on Delauney triangulation, from dense DTM, and the epipolar geometry solution applied to correct nadiral and well textured images. Two commercial software we used are Z-Map (by italian Menci Software) and Photomodeler Scanner (by canadian Eos Systems). The last goal was to test the results in relation with the archaeological diggings context and documentation purposes
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".