SLOOP is a pattern retrieval engine for Animal Biometrics that uses cloud computing, machine learning and crowd sourcing to greatly improve the study of animal movement and behavior. It works in both automated and assisted modes, determined in large measure by the errors that can be accomodated for validating or testing an ecological or biological hypothesis.
In order for scientists to get a handle on issues such as genetic variation, dispersal, diversity and movement of a species, an accurate track or capture history of individuals is needed. Historically, such counts involved a laborious process of comparing hundreds of images, often obtained by remote camera. Scientists and students spent thousands of hours poring over these images in order to identify individual animals, time arguably better spent elsewhere.
SLOOP applies pattern recognition to these same images. It then challenges online crowds to sort and identify a small proportion of potential matches. SLOOP learns from the citizen scientist’s skill to match images of individual animals more accurately and far faster than by previous methods. It is an early example of a system where the human uses the machine to accomplish a task more efficiently and the machine learns from sparse human input to get better at what it does.
SLOOP was originally designed for the marbled salamander of Western Massachusetts. Scientists around the globe rapidly understood its application to other species. Thus far, SLOOP has been adapted for use with Grand and Otago skinks in New Zealand and is in the process of being extended to many species.