Accuracy Assessment from UAS Imagery for Surface Modeling
Bibliographic record
Abstract
Small Unmanned Aircraft Systems (sUAS) have become an alternative approach for mapping and surface modeling. As technology advances that is coupled with sUAS, there has been an increase in methods developed for topographic mapping and site monitoring, particularly small to medium projects in construction and civil engineering. One of the most popular methods is image based mapping and modeling with a sUAS. This particular technique provides a dense point cloud, orthophoto, and surface model which can be used for these type of projects. However, the accuracy of photogrammetrically derived point clouds from sUAS imagery is not extensively tested. For these reasons, an evaluation was performed to assess the accuracy through a case study of a point cloud derived from sUAS imagery. A parking lot located in Ontario, California, in particular, the Citizens Bank Arena (CBA) was surveyed and used as our test site. To verify the accuracy of the sUAS derived point cloud, Ground Control Points (GCPs) were measured throughout the study area using a Global Navigation Satellite System (GNSS) Real-Time Kinematic (RTK) survey. When the GNSS-RTK survey was compared to the sUAS derived point cloud, the residuals were found to be 18 mm, and -21 mm for the horizontal and vertical components, respectively. These results from the evaluation performed indicate that sUAS derived point clouds can produce measurements that are comparable to traditional methods. In some instances, it might yield a cost-effective, safe, and efficient resolution for mapping and surface modeling in construction and civil engineering projects.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".