Compatibility Evaluation of Point Clouds Acquired with Terrestrial and Mobile LiDAR Scanners
Bibliographic record
Abstract
Light Detection And Ranging (LiDAR) is a technology that arose in the last years as one of the best technologies to capture tridimensional information about features on the Earth´s surface.LiDAR measurements can be carried out over the ground in a static mode, with the scanner fixed on a tripod.This mode is known as Terrestrial LiDAR.LiDAR measurements can also be acquired in a kinematic mode when the scanner is assembled and transported on aircrafts, cars, boats and even in special vehicles that operate in underground mines and galleries.That second mode is called Mobile LiDAR where the LiDAR scanneris connected to an Inertial Navigation System (INS) and a dual frequency GNSS receiver that respectively provide the orientation and the position of a platform and consequently the direct georeferencing.This paper focus is to compare results obtained from data collected over the same area in both scanner modes but with uncertainties.This study has used both terrestrial and mobile LiDAR scanners to generate a 3D model of the terrain of a chosen area and to calculate the volume above a predefined reference plane.The same volume was estimated with a conventional topographic technique that uses collected points in the same area using RTK -GNSS receivers.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".