利用遥感图像加强对资源不足的野生动物保护公园的偷猎预测
Enhancing Poaching Predictions for Under-Resourced Wildlife Conservation Parks Using Remote Sensing Imagery
摘要：
非法偷猎野生动物正在导致生物多样性的丧失。为了打击偷猎，护林员在广阔的保护区巡逻，寻找非法偷猎活动。然而，护林员往往无法全面搜索这样的大型公园。因此，引入了野生动物安全保护助理（PAWS），作为一种机器学习方法，以帮助确定偷猎风险最高的地区。随着PAWS部署到世界各地的公园，我们认识到许多公园用于数据汇集的资源有限，因此功能集很少。为了确保资源不足的公园能够获得有意义的偷猎预测，我们引入了使用公开的遥感数据来提取公园的特征。通过使用来自Google Earth Engine的这些数据，我们还结合了以前无法获得的动态数据，以丰富季节趋势的预测。我们自动化了整个数据到部署的管道，并发现，仅使用公开可用的数据，我们恢复的预测性能与使用公园专家手动计算的功能进行的预测相当。我们的结论是，包括卫星图像创建了一个强大的系统，通过该系统，任何资源水平的公园都可以在未来几年从偷猎风险中受益。
英文摘要：
Illegal wildlife poaching is driving the loss of biodiversity. To combat poaching, rangers patrol expansive protected areas for illegal poaching activity. However, rangers often cannot comprehensively search such large parks. Thus, the Protection Assistant for Wildlife Security (PAWS) was introduced as a machine learning approach to help identify the areas with highest poaching risk. As PAWS is deployed to parks around the world, we recognized that many parks have limited resources for data collection and therefore have scarce feature sets. To ensure under-resourced parks have access to meaningful poaching predictions, we introduce the use of publicly available remote sensing data to extract features for parks. By employing this data from Google Earth Engine, we also incorporate previously unavailable dynamic data to enrich predictions with seasonal trends. We automate the entire data-to-deployment pipeline and find that, with only using publicly available data, we recuperate prediction performance comparable to predictions made using features manually computed by park specialists. We conclude that the inclusion of satellite imagery creates a robust system through which parks of any resource level can benefit from poaching risks for years to come.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
