Satellite-based monitoring of the world’s largest terrestrial mammal migration using deep learning

Accurate, reliable, and up-to-date information on wildlife populations is crucial for biodiversity conservation in the face of unprecedented biodiversity loss worldwide. However, monitoring wildlife populations at large scales remains challenging. Advances in satellite remote sensing, particularly very-high-resolution satellite data, offer new opportunities for monitoring wildlife from space, and new machine learning techniques present great potential for detecting wildlife with remarkable speed and accuracy. Here, we introduce a deep learning pipeline for automatically detecting and counting large migratory ungulate herds (wildebeest and zebra) at the individual level in the Serengeti-Mara ecosystem from submeter-resolution satellite imagery. We apply the pipeline to implement the first-ever population census of large-size ungulates in the Serengeti-Mara ecosystem through a satellite survey and generate the total count of the whole population. The model shows robust performance across diverse landscapes with an overall F1-score of 84.75% (Precision: 87.85%, Recall: 81.86%) on an independent test dataset containing 11,594 animals and achieves good transferability spatially and temporally. This research showcases the capability of satellite remote sensing and deep learning techniques to accurately locate and count very large populations of terrestrial mammals in open landscapes. It provides a new perspective on monitoring wildlife populations and animal migration, which will facilitate the understanding of animal behavior and ecology as well as improve the conservation of the whole ecosystem in the face of rapid environmental changes.