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On 3D Dimension: Study cases for Archaeological sites

2014· article· en· W2118853789 on OpenAlexaboutno aff
Maria Grazia D’Urso, C. L. Marino, Andrea Rotondi

Bibliographic record

Venue˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · 2014
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsnot available
Fundersnot available
KeywordsGeomaticsPhotographyDimension (graph theory)Computer scienceComputer graphics (images)Cultural heritageVisualizationObserver (physics)Global Positioning SystemArtificial intelligenceComputer visionData scienceRemote sensingVisual artsGeographyArchaeologyArtMathematicsTelecommunications

Abstract

fetched live from OpenAlex

Abstract. For more than a century the tridimensional vision has been of interest for scientists and users in several fields of application. The mathematical bases have remained substantially unchanged but only the new technologies have allowed us to make the vision really impressive. Photography opens new frontiers and has enriched of physical, mathematical, chemical, informatical and topographic notions by making the images so real to make the observer fully immersed into the represented scene. By means of active googless the 3D digital technique, commonly used for video games, makes possible animations without limitations in the dimension of the images thanks to the improved performances of the graphic processor units and related hardware components. In this paper we illustrate an experience made by the students of the MSc'degree course of Topography, active at the University of Cassino and Southern Lazio, in which the photography has been applied as an innovative technique for the surveying of cultural heritage. The tests foresee the use of traditional techniques of survey with 3D digital images and use of GPS sensors. The ultimate objective of our experience is the insertion in the web, allowing us the visualization of the 3D images equipped with all data. In conclusion these new methods of survey allow for the fusion of extremely different techniques, in such an impressive way to make them inseparable and justifying the origin of the neologism "Geomatics" coined at the Laval University (Canada) during the eighties.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.286
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreMethods

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".

Quick stats

Citations11
Published2014
Admission routes1
Has abstractyes

Explore more

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