  Camera traps enable the automatic collection of large quantities of image
data. Biologists all over the world use camera traps to monitor animal
populations. We have recently been making strides towards automatic species
classification in camera trap images. However, as we try to expand the
geographic scope of these models we are faced with an interesting question: how
do we train models that perform well on new (unseen during training) camera
trap locations? Can we leverage data from other modalities, such as citizen
science data and remote sensing data? In order to tackle this problem, we have
prepared a challenge where the training data and test data are from different
cameras spread across the globe. For each camera, we provide a series of remote
sensing imagery that is tied to the location of the camera. We also provide
citizen science imagery from the set of species seen in our data. The challenge
is to correctly classify species in the test camera traps.
