Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape.

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 capabili

SUBMITTER: Wu Z 

PROVIDER: S-EPMC10224963 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature