High-resolution satellite imagery, modern digital cameras and drones have opened up new possibilities for surveying remote wildlife populations. But the huge volume of data created presents its own unique challenge for researchers; could recent advances in machine learning technology provide the answer? In a recent paper in Remote Sensing in Ecology and Conservation, an interdisciplinary team of zoologists and computer scientists from the University of Oxford and the University of Bath used machine learning to identify African elephants in high-resolution satellite images from Addo Elephant Park in South Africa and the Masai Mara in Kenya. “Elephant surveys and censuses are the bedrock of status reporting of elephant populations,” says Ben Okita-Ouma, co-chair of the IUCN-SSC African Elephant Specialist Group and director of policy and planning at Save the Elephants, a Nairobi-based NGO. “Understanding population status helps in addressing contemporary management and conservation needs of not only the animals but their habitats too.” Researchers have been exploring ways to survey animals through satellite imagery for 20 years. So far, efforts have mainly focussed on marine environments where a simple backdrop makes it easier to identify species such as whales. Some studies have looked for other useful information in satellite imagery such as measuring guano stains as a way to estimate the size of penguin colonies. In most studies to date, human observers have manually sifted through satellite images to identify animals, creating a natural limit on how much data can be processed. Researchers have used machine learning to identify African elephants…This article was originally published on Mongabay Läs mer