AI for Conservation: Aerial Monitoring to Learn and Plan Against Illegal Actors


Elizabeth Bondi. 2018. “AI for Conservation: Aerial Monitoring to Learn and Plan Against Illegal Actors .” In International Joint Conference on Artificial Intelligence (IJCAI-18) (Doctoral Consortium).


Conservation of our planet’s natural resources is of the utmost importance and requires constant innovation. This project focuses on innovation for one aspect of conservation: the reduction of wildlife poaching. Park rangers patrol parks to decrease poaching by searching for poachers and animal snares left by poachers. Multiple strategies exist to aid in these patrols, including adversary behavior prediction and planning optimal ranger patrol strategies. These research efforts suffer from a key shortcoming: they fail to integrate real-time data, and rely on historical data collected during ranger patrols. Recent advances in unmanned aerial vehicle (UAV) technology have made UAVs viable tools to aid in park ranger patrols. There is now an opportunity to augment the input for these strategies in real time using computer vision, by (i) automatically detecting both animals and poachers in UAV videos, (ii) using these detections to learn future poaching locations and to plan UAV patrol routes in real time, and (iii) using poaching location predictions to determine where to fly for the next patrol. In other words, detection is done on realtime data captured aboard a UAV. Detection will then be used to learn adversaries’ behaviors, or where poaching may occur in the future, in future work. This will then be used to plan where to fly in the long term, such as the next mission. Finally, planning where to fly next during the current flight will depend on the long term plan and the real-time detections. This proposed system directly improves wildlife security. Through our collaboration with Air Shepherd, a program of the Charles A. and Anne Morrow Lindbergh Foundation, we have already begun deploying poacher detection prototypes in Africa and will deploy further advances there in the future (Fig. 1). Furthermore, this also applies to similar surveillance tasks, such as locating people after natural disasters.
See also: 2018