@conference {1500315,
	title = {AirSim-W: A Simulation Environment for Wildlife Conservation with UAVs},
	booktitle = {In COMPASS {\textquoteright}18: ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS), June 20{\textendash}22, 2018, },
	year = {2018},
	address = {Menlo Park and San Jose, CA, USA. ACM, New York, NY, USA},
	abstract = {Increases in poaching levels have led to the use of unmanned aerial vehicles (UAVs or drones) to count animals, locate animals in parks, and even find poachers. Finding poachers is often done at night through the use of long wave thermal infrared cameras mounted on these UAVs. Unfortunately, monitoring the live video stream from the conservation UAVs all night is an arduous task. In order to assist in this monitoring task, new techniques in computer vision have been developed. This work is based on a dataset which took approximately six months to label. However, further improvement in detection and future testing of autonomous flight require not only more labeled training data, but also an environment where algorithms can be safely tested. In order to meet both goals efficiently, we present AirSim-W, a simulation environment that has been designed specifically for the domain of wildlife conservation. This includes (i) creation of an African savanna environment in Unreal Engine, (ii) integration of a new thermal infrared model based on radiometry, (iii) API code expansions to follow objects of interest or fly in zig-zag patterns to generate simulated training data, and (iv) demonstrated detection improvement using simulated data generated by AirSim-W. With these additional simulation features, AirSim-W will be directly useful for wildlife conservation research.},
	author = {Bondi, Elizabeth and Dey, Debadeepta and Kapoor, Ashish and Piavis, Jim and Shah, Shital and Fang, Fei and Dilkina, Bistra and Hannaford, Robert and Iyer, Arvind and Joppa, Lucas and Tambe, Milind}
}
