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 organisations 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. The 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 organised by species type. eMammal uses Microsoft Azure Machine Learning to categorise its massive library and guide contributors to record more accurate results. Wildlife researchers and organisations can then access these images to gain a better understanding of wildlife populations and answer critical questions about animal behaviour, reproduction, ecology, genetics, migration and conservation sustainability.

How eMammal uses Azure

eMammal’s Azure implementation was developed by Elastacloud.

Coyote photographed by a camera trap in Montana USA

Be a part of open science for all wildlife enthusiasts.

Woodchuck photographed by a camera trap in Virginia USA