Artificial intelligence is increasingly being used in the field to analyse information collected by wildlife conservationists, from camera trap and satellite images to audio recordings. AI can learn how to identify which photos out of thousands contain rare species; or pinpoint an animal call out of hours of field recordings - hugely reducing the manual labour required to collect vital conservation data.

Looking for a place to discuss camera trap troubleshooting, compare models, collaborate with members working with other technologies like machine learning and bioacoustics, or share and exchange data from your camera trap research? Get involved in our Camera Traps group! All are welcome whether you are new to camera trapping, have expertise from the field to share, or are curious about how your skill sets can help those working with camera traps. 

Used to pick up signals from tracking gear on the ground, collect images of wildlife and habitats from the air, gather acoustic data with specialized hydrophones, or even collect snot samples from whales' blowholes, drones are capable of collecting high-resolution data quickly, noninvasively, and at relatively low cost.

Footprints are everywhere, and are perhaps the most overlooked source of data on the planet.  WildTrack's Footprint Identification Technique (FIT) can identify species, individuals, sex and age-class to a high level of accuracy from simple images of footprints taken to a standardized protocol. This technique has the benefit of being non-invasive, cost-effective and draws on the strengths of community-skills such as tracking and observation. Our WildTrackFIT community is composed of users in >20 countries and we have FIT species algorithms developed for a range of species from big cats to Pachyderms, bears, mustelids, and even small mammals.If you see footprints as part of your fieldwork, or in another capacity, we'd love to hear from you!