# Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations

arXiv: https://arxiv.org/abs/1903.06669 · HTML: https://mlsanity.com/paper/1903.06669/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2019.

## Built on by (most cited first, 11 of 11)

- [Artificial Replay: A Meta-Algorithm for Harnessing Historical Data in Bandits](https://mlsanity.com/paper/2210.00025/) (2022)
- [Robust Reinforcement Learning Under Minimax Regret for Green Security](https://mlsanity.com/paper/2106.08413/) (2021)
- [Domain constraints improve risk prediction when outcome data is missing](https://mlsanity.com/paper/2312.03878/) (2023)
- [Robust Optimization with Diffusion Models for Green Security](https://mlsanity.com/paper/2503.05730/) (2025)
- [Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning](https://mlsanity.com/paper/2106.03279/) (2021)
- [Ranked Prioritization of Groups in Combinatorial Bandit Allocation](https://mlsanity.com/paper/2205.05659/) (2022)
- [Reflections from the Workshop on AI-Assisted Decision Making for Conservation](https://mlsanity.com/paper/2307.08774/) (2023)
- [Facilitating human-wildlife cohabitation through conflict prediction](https://mlsanity.com/paper/2109.10637/) (2021)
- [Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2112.12458/) (2021)
- [WildGraph: Realistic Graph-based Trajectory Generation for Wildlife](https://mlsanity.com/paper/2404.08068/) (2024)
- [Fast and the Furious: Hot Starts in Pursuit-Evasion Games](https://mlsanity.com/paper/2510.10830/) (2025)

## Abstract (arXiv)

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.