Accuracy Assessment from UAS Imagery for Surface Modeling
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
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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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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