Mobile 3D Mapping for Surveying Earthwork Using an Unmanned Aerial Vehicle (UAV)
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Bibliographic record
Abstract
Mobile 3D Mapping for Surveying Earthwork Using an Unmanned Aerial Vehicle (UAV) Sebastian Siebert, Jochen Teizer Pages 1366-1375 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) 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). Keywords: Aerial surveying, camera, geomatics, laser scanning, mapping, photogrammetry, range point cloud, total station, safety, surveying, unmanned aerial vehicle (UAV), vision sensing DOI: https://doi.org/10.22260/ISARC2013/0154 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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