Applicability and limitations of terrestrial and UAV-based remote sensing techniques for slope rock mass characterization
Bibliographic record
Abstract
ABSTRACT: This study evaluates the applicability and limitations of terrestrial and UAV-based remote sensing techniques in rock mass assessments. Terrestrial laser scanning (TLS) and two portable laser scanners (HandyScan and GeoSLAM) were employed as ground-based methods, offering high-resolution data and detailed local 3D models. UAV-based LiDAR and photogrammetry techniques were integrated to capture slope-scale topographic variations and rock mass structure. These methods were evaluated for their ability to capture key rock mass parameters such as RQD, joint spacing, aperture, roughness, and joint orientation. Each method's accuracy, point density, data acquisition time, and suitability for varied geological conditions were analyzed. TLS and HandyScan provided high-resolution data, making them suitable for detailed surface assessments, while GeoSLAM enabled efficient scanning of complex terrains with mobile capabilities. UAV-based LiDAR and photogrammetry allowed rapid and broad data collection, particularly in inaccessible areas, but faced limitations in capturing fine-scale features in vegetated areas. The findings highlight the strengths and limitations of each technique and provide recommendations for their application for rock mass characterization and slope stability analysis in slope cut assessments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".