Integration of laser scanning and close-range photogrammetry -- The last decade and beyond
Bibliographic record
Abstract
In last decade, we have witnessed an increased number of publications related to systems that combine laser scanning and close-range photogrammetry technologies in order to address the challenges posed by application fields as diverse as industrial, automotive, space exploration and cultural heritage to name a few. The need to integrate those technologies is driven by resolution, accuracy, speed and operational requirements, which can be optimized using general techniques developed in the area of multisensor and information fusion theory. This paper addresses an aspect critical to multi-sensor and information fusion, i.e., the estimation of systems uncertainties. The understanding of the basic theory and best practices associated to laser range scanners, digital photogrammetry, processing, modelling are in fact fundamental to fulfilling the requirements listed above in an optimal way. In particular, two categories of applications are covered, i.e., information augmentation and uncertainty management. Results from both space exploration and cultural heritage applications are shown.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".