Machine Learning for Wildlife Conservation with UAVs

Poaching has increased in the last decade, particularly poaching of elephants and rhinoceroses. Current levels of poaching of elephants and rhinoceroses will lead to their extinction in the next 10 years. Unmanned aerial vehicles (UAVs) can be flown to spot poachers or to locate animals.

Animal collage image

AirShepherd

One of our collaborators for this project is a non-governmental organization (NGO) called AirShepherd. AirShepherd flies UAVs in Africa equipped with a thermal infrared camera to identify and intercept poachers at night, when poaching activity typically occurs, before they can harm wildlife.

Machine Learning Research

We are working to automatically detect poachers and animals in thermal infrared images using deep learning techniques. We are also interested in planning UAV patrol routes. We continue to be interested in recruiting students to join us in this project.

SPOT is a tool that automatically detects poachers in long wave thermal infrared UAV videos. Tests of SPOT have been run by AirShepherd at a testing site in South Africa, where training exercises take place. This video is sped up 20 times and shows a 30 minute test at the site using Microsoft Azure on a slow internet connection.

Demonstration of SPOT detecting poachers in the AirSim-W Africa environment

A drone is following poachers through the AirSim-W Africa environment. The normal drone view is on the left, and the corresponding thermal infrared simulation is on the right. SPOT is looking for poachers in the simulated thermal infrared image, and poacher detections are shown in blue boxes. Now, we can see how SPOT would work in the field using a simulated environment.

Undegraduate Students

  • Apurva Gandhi
  • Anthony Nelson
  • Diane Reed
  • Lucas Hu
  • Lauren Potterat
  • Suraj Swarup

Collaborators:

Former students:

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