CSV Update
CSV file exports have been significantly sped up, along with significant simplification of the custom-format form.
New Southern African and South American Species Classifiers
New species classifiers for Southern Africa and South America have been released – both increasing classification accuracy and species coverage.
TrapTagger Waterholes Update
In this update, we add a number of features focused in improving the processing of waterhole-type data where animals can spend extended periods in front of the camera triggering many images. We achieve this through the addition of improved clustering, more-strict static-detection definitions for waterhole data, and a new related-images annotation workflow.
AI contributes to species conservation with TrapTagger
Picture a conservation biologist in the savannah, you’d be forgiven for imagining a scientist in camouflage fatigues with a pair of binoculars in one hand and a notebook in the other, but this image, which so readily comes to mind, may soon be consigned to a bygone era. Notably in South Africa, where specialists like Nicholas Osner are leveraging artificial intelligence to usher in new approaches to the task of conservation. The machine-learning engineer explains: “We have developed a web application called TrapTagger, which uses an open-source algorithm to automatically process images from camera traps”, devices that can easily be attached to trees to capture pictures of wildlife. TrapTagger is just one of the projects pioneered by WildEye Conservation, a non-profit that develops AI-based tools to facilitate the conservation of species. The system can also detect the presence of humans and vehicles, but the crucial advantage of this innovation is its ability to analyse images and recognise the species that appear in them.
A camera trap grid runs through it: surveying Etosha wildlife large scale and long term!
One of the many challenges that wildlife biologists and managers face is to know how many animals occur in a given area, how they distribute themselves in space and how this distribution changes with seasons. Estimating ungulate population size and structure and their spatial distribution is traditionally done using road or aerial surveys. However, when one is also interested in how these 3 parameters vary in time, at the seasonal or annual scales for instance, these survey methods quickly become extremely costly and logistically unpractical, especially over an area as large as the entire Etosha National Park (~23 000 km2)!