Terrestrial LiDAR Capabilities for 3D Data Acquisition (Indoor and Outdoor) in the Context of Cadastral Modelling: A Comparative Analysis for Apartment Units
Bibliographic record
Abstract
The paper presents a comparison of terrestrial LiDAR and Distancemeter for surveying 3D spatial data of property units (indoor and outdoor) and producing cadastral representations (2D and 3D). Two study sites representing apartment buildings (co-ownership units) were surveyed with both instruments and six criteria related to data acquisition steps (survey time, number of measures, number of operators) and data modeling steps (preprocessing time, time for modelling the geometry of the objects, completeness) are used to enable the comparison. To produce 2D maps LiDAR technology ended with performance in term of survey and modeling time a little lower compare to Distancemeter. To produce 3D models LiDAR technology shows better results compare to Distancemeter. The number of objects to model and the geometric complexity of these objects are important criteria to take into consideration to determine the advantages of LiDAR technology compared to traditional instruments. For instance, LiDAR point cloud offers the possibility of producing more detailed 3D model (i.e. containing not only cadastral limits).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".