Unmanned Aircraft Systems in Civil Engineering Applications
Bibliographic record
Abstract
Three-dimensional city modeling has gained interest recently due to its useful applications. It plays an important role in decision-making and can be used comprehensively for urban planning analysis, disaster management, and tourism. One particular field, where it is highly important, is in civil and construction engineering. The opportunity to utilize such information has not only improved efficiency but provided the opportunity to explore innovative solutions and offer alternate options in decision making. Unmanned Aircraft Systems (UAS) are emerging as an alternative tool for mapping cities at a medium scale. The investigation of the dense point cloud created from imagery acquired by these systems is required to understand the limitations and accuracy of the derived product. For this reason, an analysis was performed to test the capabilities of UAS based mapping for city modeling and surface reconstruction. In this study, a UAV was flown over the Westland Group Building in Ontario, CA. The drone collected overlapping images during a preset trip using an IOS application, which was processed on ContextCapture. The UAS image-based results are compared to a high-resolution surface model that was acquired by Terrestrial Laser Scanning (TLS). At this point, the data is being compared between the two systems, by performing cloud-to-cloud comparison on CloudCompare. Preliminary analysis reveals that the mean vertical distance discrepancy between the UAS and the TLS is 0.07 ft with a standard deviation of 0.721 ft, which paves a promising path for UAS applications in city modeling.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".