Comparison of SfM computer vision point clouds of a landslide derived from multiple small UAV platforms and sensors to a TLS-based model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Structure from motion (SfM) computer vision is a remote sensing method that is gaining popularity due to its simplicity and ability to accurately characterize site geometry in three dimensions (3D). While many researchers have demonstrated the potential for SfM to be used with unmanned aerial vehicles (UAVs) to model in 3D various geologic features, such as landslides, little is understood concerning how the selection of the UAV platform can affect the resolution and accuracy of the model. This study evaluates the resolution and accuracy of 3D point cloud models of a large landslide that occurred in 2013 near Page, Arizona, that were developed from various small UAV platform and camera configurations. Terrestrial laser scans were performed at the landslide and were used to establish a comparative baseline model. Results from the study indicate that point cloud resolution improved by more than 16% when using multi-rotor UAVs instead of fixed-wing UAVs. However, accuracy of the points in the point cloud model appear to be independent of the UAV platform, but depend principally on the selected camera and the image resolution. Additional practical guidance on flying various UAV platforms in challenging field conditions is provided for geologists and engineers.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it