Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations (Short Version)
Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations (Short Version)
复制标题
领先偷猎者：不确定性下的非法野生动物偷猎预测和巡逻计划与现场测试评估（简短版）
DOI：
10.1109/icde48307.2020.00198
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Eric Enyel
中科院分区：
文献类型：
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作者：
Shahrzad Gholami;Lily Xu;S. M. Carthy;B. Dilkina;A. Plumptre;Milind Tambe;Rohit Singh;Mustapha Nsubaga;Joshua Mabonga;M. Driciru;F. Wanyama;A. Rwetsiba;Tom Okello;Eric Enyel
非法偷猎野生动物威胁着生态系统，使濒危物种濒临灭绝。然而，野生动物保护工作受到执法机构资源有限的制约。为了帮助打击偷猎，野生动物安全保护助理（PAWS）是一个机器学习管道，已被开发为数据驱动的方法，以确定整个保护区偷猎风险高的区域，并计算最佳巡逻路线。在本文中，我们采用端到端的方法来实现数据到部署管道的反偷猎。在这样做的过程中，我们解决的挑战，包括极端的类不平衡（高达1：200），偏见和不确定性的野生动物偷猎数据，以提高PAWS，我们应用我们的方法，以三个国家公园具有不同的特点。(i)我们使用高斯过程来量化预测的不确定性，我们利用它来提高我们规定的巡逻的鲁棒性，并将圈套的检测平均提高30%。我们评估我们的方法在现实世界的历史偷猎数据默奇森福尔斯和伊丽莎白女王国家公园在乌干达，并首次在柬埔寨Srepok野生动物保护区。(ii)我们提出了在默奇森福尔斯和Srepok野生动物保护区进行的大规模实地测试的结果，证实了PAWS的预测能力有望扩展到多个公园。本文是通过与SMART保护软件集成将PAWS扩展到世界各地800个公园的努力的一部分。
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.