Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations
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Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
Illegal wildlife poaching threatens ecosystems and
drives endangered species toward extinction. However, efforts for
wildlife protection are constrained by the limited resources of law
enforcement agencies. To help combat poaching, the Protection
Assistant for Wildlife Security (PAWS) is a machine learning
pipeline that has been developed as a data-driven approach to
identify areas at high risk of poaching throughout protected
areas and compute optimal patrol routes. In this paper, we
take an end-to-end approach to the data-to-deployment pipeline
for anti-poaching. In doing so, we address challenges including
extreme class imbalance (up to 1:200), bias, and uncertainty
in wildlife poaching data to enhance PAWS, and we apply our
methodology to three national parks with diverse characteristics.
(i) We use Gaussian processes to quantify predictive uncertainty,
which we exploit to improve robustness of our prescribed patrols
and increase detection of snares by an average of 30%. We
evaluate our approach on real-world historical poaching data
from Murchison Falls and Queen Elizabeth National Parks in
Uganda and, for the first time, Srepok Wildlife Sanctuary in
Cambodia. (ii) We present the results of large-scale field tests
conducted in Murchison Falls and Srepok Wildlife Sanctuary
which confirm that the predictive power of PAWS extends
promisingly to multiple parks. This paper is part of an effort to
expand PAWS to 800 parks around the world through integration
with SMART conservation software.
Description
Keywords
Wildlife protection, Data mining, Predictive modeling, Patrol route planning, Poaching, Uncertainty
Citation
Xu, L., Gholami, S., McCarthy, S., Dilkina, B., Plumptre, A., Tambe, M., ... & Enyel, E. (2020, April). Stay ahead of Poachers: Illegal wildlife poaching prediction and patrol planning under uncertainty with field test evaluations (Short Version). In 2020 IEEE 36th International Conference on Data Engineering (ICDE) (pp. 1898-1901). IEEE.