eMammal is an Azure-based platform that stores and organizes images from citizen-run camera traps while learning more about the world’s land mammals.Learn about eMammal
What does it mean for the ecosystem when invasive feral pigs tear through an area? What is the correlation between deer populations and spreading disease? Researchers hope to answer questions like these by studying local wildlife, but their organizations are critically underfunded—and they need help. Citizen scientists have the means to record valuable photos and data to help scientists tackle these complex conservation challenges. Their contributions are crucial to understanding and rehabilitating the world’s land mammal population.
eMammal is a data management system and archive for camera trap research projects. Created and managed by the Smithsonian Conservation Biology Institute and the North Carolina Museum of Natural Sciences, and supported by Microsoft AI for Earth, the eMammal platform makes wildlife photography accessible, opening the door for citizen scientists to contribute their own images to environmental research efforts. Contributors upload images captured from at-home camera traps to eMammal, where the images are tagged and organized by species type. eMammal uses machine learning to categorize its massive library and guide contributors to record more accurate results. Wildlife researchers and organizations can then access these images to gain a better understanding of wildlife populations and answer critical questions about animal behavior, reproduction, ecology, genetics, migration, and conservation sustainability.
A window into the secret life of ecosystems
Camera traps allow scientists to observe wildlife in their native environment. Photo data collected from thousands of cameras can allow scientists to understand complicated patterns of migration, changes in populations, behavior in the wild, and animal interaction. AI helps process this vast amount of data, allowing scientists to better understand these complex interactions and conserve ecosystems at risk.
How eMammal works
Contributors and partners set up camera traps to snap photos of passing animals. The images are uploaded and tagged through the eMammal app. The Azure-based platform then processes the metadata and images, and machine learning identifies which images contain animals. The data and images can be used by scientists and the public on the eMammal site.