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.

中文翻译：

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利用遥感图像增强资源匮乏的野生动物保护公园的盗猎预测

非法偷猎野生生物正在加剧生物多样性的丧失。为了与偷猎作斗争，护林员在广阔的保护区巡逻，进行非法偷猎活动。但是，护林员通常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手（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被部署到世界各地的公园中，我们认识到许多公园的数据收集资源有限，因此缺乏功能集。为了确保资源贫乏的公园可以获取有意义的盗猎预测，我们引入了使用公共遥感数据提取公园特征的信息。通过使用来自Google Earth Engine的数据，我们还结合了以前不可用的动态数据，以丰富季节趋势的预测。我们将整个数据部署流程自动化，发现仅使用公开可用的数据，我们就可以恢复与公园专家手动计算的功能相当的预测性能。我们得出的结论是，纳入卫星图像会创建一个健壮的系统，任何资源级别的公园都可以通过该系统受益于未来几年的盗猎风险。