New Technologies Could Help Conservationists Keep Better Track of Serengeti Wildebeest Herds

Below is a press release about the Methods in Ecology and Evolution article ‘A comparison of deep learning and citizen science techniques for counting wildlife in aerial survey images‘ taken from the University of Glasgow.

A wildebeest herd in the Serengeti. ©Daniel Rosengren

A wildebeest herd in the Serengeti. ©Daniel Rosengren

Mathematicians and conservationists from the UK, Africa and the United States have used machine-learning and citizen science techniques to accurately count wildebeest in the Serengeti National Park in Tanzania more rapidly than is possible using traditional methods.

Evaluating wildebeest abundance is currently extremely costly and time-intensive, requiring manual counts of animals in thousands of aerial photographs of their habitats. From those counts, which can take months to complete, wildlife researchers use statistical estimates to determine the size of the population. Detecting changes in the population helps wildlife managers make more informed decisions about how best to keep herds healthy and sustainable. Continue reading