Deployment of an unmanned aerial system to assist in mapping an intermittent stream
Bibliographic record
Abstract
Abstract Recent growth in the capabilities of unmanned aerial vehicles and systems (UASs) as airborne platforms for collecting environmental data has been very rapid. There are now ample examples in the literature of UASs being deployed to map fine‐scale vegetation, glacial, soil and atmospheric conditions. The purported advantages of UASs are their ability to collect spatial data at lower cost, lower risk, higher resolution and higher frequency than ground surveys or satellite platforms. In this specific study, whether or not obtaining high‐resolution UAS imagery was advantageous for identifying an intermittent stream network was determined by comparing it with coarse‐scale satellite imagery collected for the same purpose. It was also determined if the UAS imagery could be an improvement to Global Positioning System acquired ground‐truth points for classifying an intermittent stream network across the same large‐scale satellite image. The UAS‐acquired and satellite‐acquired imageries were derived from a visible spectrum camera capable of 2‐cm resolution and multispectral SPOT‐5 with 10‐m resolution, respectively. The SPOT‐5 imagery with its relatively coarse resolution could not always detect the narrow intermittent stream, which was well resolved in the UAS imagery. When a classified UAS image was applied as a training area for the SPOT‐5 image, the identification of the stream network and accuracy of the satellite imagery classification did not necessarily improve. UASs have the potential to revolutionize hydrological research the same way that geographic information systems did three decades ago. A final goal of the paper is to provide insight into the advantages and disadvantages of deploying a UAS for this kind of research. © 2015 Her Majesty the Queen in Right of Canada. Hydrological Processes. © 2015 John Wiley & Sons, Ltd.
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 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 | 0.000 | 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) | 0.001 | 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 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".