Using high spatial resolution satellite imagery (PlanetScope) to count moored marine vessels in desolation sound marine provincial park
Bibliographic record
Abstract
This study aims to assess the applicability of using high spatial resolution spaceborne imageries (PlanetScope with a 3-meter-pixel size) to monitor the number of marine vessels moored nearshore in Desolation Sound Marine Provincial Park, British Columbia, Canada during the summer and early fall of 2022. The purpose of this study is to understand the number of vessels moored near eelgrass habitat, a critical plant species in the park threatened by boating activities. Twelve trap cameras were set up to capture marine vessel activities during the summer, and filtering and manual counting were conducted to ensure the absolute accuracy of the data. Comparisons are made between the marine vessel counts from trap cameras and satellite imagery for each day throughout the period of study. The results indicate that the use of high spatial resolution spaceborne imageries to monitor the number of marine vessels is applicable and demonstrates 100% accuracy in the range of five undercounts or overcounts. However, during the peak boating season, precision fluctuates due to undercounts, which happen more frequently than precise count or overcounts. Further studies are recommended by ensuring the functionality and increasing the coverage area of trap cameras. Spaceborne imageries with higher-resolution can also be used if cost is not a concern.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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; 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".