A New Method for Change Detection in Stone and Concrete Structures with Digital Photogrammetry
Bibliographic record
Abstract
Point clouds, generated by Photogrammetry and LiDAR, allow us to detect far more complex deformation processes, with greater speed and accuracy, than previous surveying techniques. The accuracy of any monitoring is determined, in part, by the method used to align the two epochs in the same space. Surveying equipment can be used to stake-out ground control points visible in either the laser scan or the photogrammetry and so provide absolute positioning for each epoch. As an alternative, 3D shape-matching algorithms like RANSAC and Iterative Closest Point (ICP) can be used downstream to align the two epochs based only on the invariant features visible in the dense clouds. Shape-matching is, however, limited in accuracy because a) operator proficiency in guiding the algorithms leaves room for error, and b) the point clouds used for alignment are usually insufficiently dense to guarantee sub-centimeter change detection. Proposed is a new method of alignment between monitoring epochs for photogrammetry. Photogrammetry has the ability to match features between images down to the accuracy of 0.15 pixels, and provides us with a robust statistical model to predict both accuracy and error. Using invariant features in photographs shared between monitoring epochs, we can use the algorithms of Normalized Cross Correlation and Least-Squares Matching to very accurately "pin" one monitoring epoch to another. In a series of simple experiments we demonstrate that sub-centimeter change detection is easily accomplished on highly textured surfaces such as stone and concrete. This has applications in Archaeology, Architecture, and Civil Engineering surveys.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".