ArticleNature communications2023
Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Article
- From data to decisions: The role of machine learning in precision ecological restoration.Ecological applications : a publication of the Ecological Society of America · 2026Review
- Dynamic Lake Ice Conditions Shape Caribou Water-Crossing Behavior in the Arctic.Global change biology · 2026Article
- Harnessing Population Genomics, Gut Microbiota, and Environmental DNA Surveillance for the Conservation of Chinese Spotted Seals in a Changing World.Ecology and evolution · 2026Review
- Spatial patterns of cattle densities across the Brazilian Amazon revealed by very high-resolution satellite imagery.Communications sustainability · 2026Article
- Large-scale remote sensing model enables an integrated monitoring approach for high-resolution tracking pest vole populations.Scientific reports · 2025Article
- Automated rhinoceros detection in satellite imagery using deep learning.Scientific reports · 2025Article
- Probabilistic machine learning for noisy labels in Earth observation.Scientific reports · 2025Article
- AI-based satellite survey offers independent assessment of migratory wildebeest numbers in the Serengeti.PNAS nexus · 2025Article
- Future scientific paradigms in the integration of materials, aerospace and information.National science review · 2025Article
- WildDrone: autonomous drone technology for monitoring wildlife populations.Frontiers in robotics and AI · 2025Article
- Reptile Identification for Endemic and Invasive Alien Species Using Transfer Learning Approaches.Sensors (Basel, Switzerland) · 2024Article
- Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape.Nature communications · 2023Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented speed and precision. These efficiencies promise to reveal novel ecological insights at spatial scales which are germane to the management of populations and entire ecosystems. Here, we present a robust transferable deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine-resolution (38-50 cm) satellite imagery. The results achieve accurate detection of nearly 500,000 individuals across thousands of square kilometers and multiple habitat types, with an overall F1-score of 84.75% (Precision: 87.85%, Recall: 81.86%). This research demonstrates the capability of satellite remote sensing and machine learning techniques to automatically and accurately count very large populations of terrestrial mammals across a highly heterogeneous landscape. We also discuss the potential for satellite-derived species detections to advance basic understanding of animal behavior and ecology.
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