Mobile 3D Mapping for Surveying Earthwork Using an Unmanned Aerial Vehicle (UAV)
Bibliographic record
Abstract
Unmanned Aerial Vehicles (UAV) as a data acquisition platform and as a measurement instrument have become attractive for many surveying applications in civil engineering.However, their performance is not well understood for these particular applications.The specific scope of the presented work is the performance evaluation of a UAV system that was built to rapidly acquire mobile 3D mapping data for large earthmoving construction sites.Details to the components of the developed system (hardware and control software) are explained.A novel program for photogrammetric flight planning and its execution for the generation of 3D point clouds from digital mobile images is explained.A performance model for estimating the position error was developed and tested in several realistic construction environments.Results to these tests are presented as they relate in particular to large excavation and earth moving construction sites.Results and experiences with the developed UAV system are in particular useful for researchers or practitioners in need for successfully adapting UAV technology for their application(s).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".