  In this paper we describe an unmanned aerial system equipped with a
thermal-infrared camera and software pipeline that we have developed to monitor
animal populations for conservation purposes. Taking a multi-disciplinary
approach to tackle this problem, we use freely available astronomical source
detection software and the associated expertise of astronomers, to efficiently
and reliably detect humans and animals in aerial thermal-infrared footage.
Combining this astronomical detection software with existing machine learning
algorithms into a single, automated, end-to-end pipeline, we test the software
using aerial video footage taken in a controlled, field-like environment. We
demonstrate that the pipeline works reliably and describe how it can be used to
estimate the completeness of different observational datasets to objects of a
given type as a function of height, observing conditions etc. -- a crucial step
in converting video footage to scientifically useful information such as the
spatial distribution and density of different animal species. Finally, having
demonstrated the potential utility of the system, we describe the steps we are
taking to adapt the system for work in the field, in particular systematic
monitoring of endangered species at National Parks around the world.
