Combination of UAV-borne LiDAR and UAV-borne photogrammetry to assess slope stability
Bibliographic record
Abstract
Monitoring and assessing slope stability quickly and accurately over large areas is a technical challenge.In this study, we investigated the combination of two UAV/UAS (Unmanned Aerial Vehicle or System) techniques to compensate for the limitations of each remote sensing technique, in the context of the Belchatow lignite mine (central Poland).The test site is one of the largest open-pit mines in Europe.It is characterized by complex geological and mining conditions (Rybicki, 1996) and has experienced numerous slope failures.Its western slope, located near the Debina salt dome, requires specific mine design to ensure safe and effective lignite exploitation.In this experiment, which is a first on this scale, two UAV-borne LiDAR and UAV-borne photogrammetry surveys were performed, the first in November 2016 and the second in March 2017.The occurrence of a landslide between these two campaigns allowed to precisely identify and study the impacted zone in terms of surface displacements, slip surfaces and fractures changes.It allowed also confirming previous results about the mine stability issues in the covered area, where a geotechnical monitoring was installed just before the first UAV-borne LiDAR and UAV-borne photogrammetry.Conclusions regarding the complementary approaches set-up were also brought, along with practical recommendations for monitoring and rapidly asses slope stability, particularly for inaccessible areas.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".