标题： 领先于偷猎者：基于不确定性的非法野生动物偷猎预测与巡逻规划及实地测试评估 显示英文标题

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

摘要： 非法野生动物偷猎威胁生态系统，并推动濒危物种走向灭绝。然而，执法机构资源有限，制约了野生动物保护工作的开展。为了帮助打击偷猎行为，野生动物安全保护助手（PAWS）作为一种机器学习管道，已被开发为一种以数据驱动的方法，用于识别保护区内的高风险偷猎区域并计算最优巡逻路线。 在本文中，我们采取了一种端到端的方法来处理反偷猎的数据到部署流程。通过这种方式，我们解决了包括极端类别不平衡（高达1:200）、偏见和野生动物偷猎数据中的不确定性等问题，以增强PAWS的功能，并将我们的方法应用于三个具有不同特征的国家公园。 (i) 我们使用高斯过程量化预测不确定性，利用这一点提高我们规定巡逻的鲁棒性，并平均增加30%的地笼检测率。我们在乌干达穆奇森瀑布和皇后伊丽莎白国家公园的真实历史偷猎数据上评估了我们的方法，并首次在柬埔寨色雷波野生动物保护区进行了评估。 (ii) 我们展示了在穆奇森瀑布和色雷波野生动物保护区进行的大规模实地测试结果，这些结果显示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扩展到全球800个公园的努力的一部分。 显示英文摘要

摘要： 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.