AI-based satellite survey offers independent assessment of migratory wildebeest numbers in the Serengeti
Bibliographic record
Abstract
The Great Wildebeest Migrationin the Serengeti-Mara ecosystem is a globally iconic wildlife phenomenon that supports the health and biodiversity of the region by supporting predator populations, regulating herbivore densities, and driving nutrient cycling. This study presents the first AI-powered satellite survey, using two deep learning-based models (U-Net and YOLOv8) to detect and count wildebeest over more than 4,000 km² across two consecutive years in August 2022 and 2023 with F1 scores reaching 0.830 (Precision: 0.832, Recall: 0.838). The satellite-based results show fewer than 600,000 individuals-approximately half the widely cited estimate of 1.3 million wildebeest, which has remained largely unchanged since the 1970s. While some variation may arise from differences in spatial and temporal coverage between survey methods, the satellite approach employs rigorously validated AI models with demonstrated accuracy. Rather than undermining previous methods, this discrepancy underscores the importance of using independent and complementary monitoring tools to refine population estimates and improve our understanding of wildebeest movement dynamics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".