Semi‐Automated Detection of Right Whales ( <i>Eubalaena</i> <scp>spp.</scp> ) in Very High‐Resolution Satellite Imagery
Bibliographic record
Abstract
ABSTRACT Space‐based detection of whales is proliferating because it shows promise as a monitoring tool, yet tests of its application across taxa and environments are rare. The objective of this study was to develop an end‐to‐end, semi‐automated procedure for detecting two right whale species ( Eubalaena glacialis and E. australis ) in satellite imagery. We collected 35 new and archived images covering 5200 km 2 of ocean in three habitat types from various areas around the globe, then constructed and tested an EfficientNet model classifier and a Faster Region‐Based Convolutional Neural Network detection model to process the imagery. The model was trained using 428 large whales manually detected in 18 images. The test set included 119 large whales found in 5 images collected within right whale habitats that were not included in the training set. The model produced strong recall at detecting whales (> 0.73). Precision was lower in northern temperate E. glacialis habitat (0.11–0.20) than in coastal tropical E. australis habitat (0.84–0.95). Satellite tasking, detection, and species‐level identification efforts were more successful in coastal than in open‐ocean habitats. The end‐to‐end procedure was effective at detecting large whales in satellite imagery. Moving forward, responsive tasking options and the high price of imagery are challenges to scaling up this procedure for operational monitoring.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".