Machine Learning for Wildlife Conservation with UAVs

Biplane flying over vegetation

Machine Learning for

Wildlife Conservation with UAVs

 

 

PREVENTING EXTINCTION USING UNMANNED AERIAL VEHICLES (UVAs)

Elephants & Rhinoceros

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.

  

AIR SHEPHERD

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 Air Shepherd 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

  

MACHINE LEARNING RESEARCH

SPOT tools

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 Air Shepherd 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

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.

 

BIRDSAI DATASET

The Benchmarking IR Dataset for Surveillance with Aerial Intelligence (BIRDSAI, pronounced "bird's-eye") is the long-wave thermal infrared dataset containing nighttime images of animals and humans in Southern Africa used for this research. The dataset allows for benchmarking of algorithms for automatic detection and tracking of humans and animals with both real and synthetic videos. 

More information can be found on the dataset homepage. The data can be downloaded from the Labeled Information Library of Alexandria. We also provide a brief video description. 

 

 

STRATEGIC DEPLOYMENT AND SIGNALING

Where should the limited number of conservation drones fly to best protect animals? Can anything be done to deter poaching if park rangers are unable to reach a target quickly? We address these questions using game theory, and show that strategic signaling can deter poaching both theoretically and in simulation.

 

PROJECT
PARTICIPANTS

Undegraduate Students

Ilkin Bayramli

Former students

Venil Loyd Noronha
Donnabell Dmello
Ajay Anand
Ankita Agarwal
Apurva Gandhi
Anthony Nelson
Diane Reed
Lucas Hu
Lauren Potterat
Suraj Swarup
Adithya Bellathur
Zahra Surani

  

SPONSORS

Contact us about being a sponsor for this project
 

RELATED
PUBLICATIONS

Proceedings of the Eighth Conference on Decision and Game Theory for Security (GameSec), 2017

Elizabeth Bondi, Fei Fang, Debarun Kar, Venil Noronha, Donnabell Dmello, Milind Tambe, Arvind Iyer, and Robert Hannaford

VIOLA: Video Labeling Application for Security Domains

Proceedings of the Thirtieth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI), 2018

Elizabeth Bondi, Fei Fang, Mark Hamilton, Debarun Kar, Donnabell Dmello, Jongmoo Choi, Robert Hannaford, Arvind Iyer, Lucas Joppa, Milind Tambe, Ram Nevatia

SPOT Poachers in Action: Augmenting Conservation Drones with Automatic Detection in Near Real Time

Proceedings of the ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS), 2018

Elizabeth Bondi, Debadeepta Dey, Ashish Kapoor, Jim Piavis, Shital Shah, Fei Fang, Bistra Dilkina, Robert Hannaford, Arvind Iyer, Lucas Joppa, Milind Tambe

AirSim-W: A Simulation Environment for Wildlife Conservation with UAVs

Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), 2020

Elizabeth Bondi, Raghav Jain, Palash Aggrawal, Saket Anand, Robert Hannaford, Ashish Kapoor, Jim Piavis, Shital Shah, Lucas Joppa, Bistra Dilkina, Milind Tambe

BIRDSAI: A Dataset for Detection and Tracking in Aerial Thermal Infrared Videos

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020

Elizabeth Bondi, Hoon Oh, Haifeng Xu, Fei Fang, Bistra Dilkina, Milind Tambe

To Signal or Not To Signal: Exploiting Uncertain Real-Time Information in Signaling Games for Security and Sustainability