CyberSEES: Type 2: Collaborative Research: Cyber-infrastructure and Technologies to Support Large-Scale Wildlife Monitoring and Research for Wildlife and Ecology Sustainability

将人类活动对野生动物的影响降至最低是保护生物学的优先事项。分布式运动感应型相机，或称相机陷阱，是监测野生动物种群的流行工具。最近的工作表明，相机陷阱调查可以通过公民科学的众包来扩大到大规模，产生评估可持续发展战略对野生动物种群的影响所需的大数据集。然而，这些大规模的调查产生了数百万张照片，给数据处理和质量控制带来了新的挑战。该项目旨在开发基于云的大规模数据传感、分析、注释、管理和保护野生动物和生态可持续性的先进计算技术，为有效的资源管理、决策和人类保护野生动物和自然资源的行动提供信息。具体地说，该项目的目标是：(1)探索用于大规模可持续数据收集的以公民科学家为基础的方法和系统；(2)开发基于深度学习的大数据集和自动化内容注释的细粒度动物物种识别；(3)研究基于云计算的瘦客户端访问和资源分配，以实现可扩展的部署和公民科学家的轻松访问；以及(4)建立紧密结合计算机注释、众包和专家审查的综合数据注释质量监测和控制框架，以确保数据质量的高科学标准。这些工具将被整合到eMammal基础设施中，以研究野生动物可持续发展的三个问题：能源开发、住房开发和野生动物收获。使用eMammal系统的其他野生动物研究人员也可以使用这些工具，从而更好地理解人类如何与野生动物可持续地生活在一起。在大的地理和时间尺度上对野生动物进行协作监测和跟踪将有助于理解野生动物系统的复杂动态，并为做出明智的决策和有效解决野生动物环境中的可持续性问题提供重要的科学证据。这个项目将提供独特的、令人兴奋的和跨学科的机会来指导研究生，并让K-12和本科生参与到专业指导的研究中。这个项目中使用的公民科学方法应该可以容纳数百名学生进行研究。

Minimizing the impact of human actions on wildlife is a priority for conservation biology. Distributed motion-sensitive cameras, or camera traps, are popular tools for monitoring wildlife populations. Recent work has shown that camera trap surveys can be expanded to large scales by crowdsourcing through citizen science, producing big data sets needed to evaluate the effect of sustainability strategies on wildlife populations. However, these large-scale surveys create millions of photographs that create new challenges for data processing and quality control. This project seeks to develop advanced computing technologies for cloud-based large-scale data sensing, analysis, annotation, management, and preservation for wildlife and ecological sustainability to inform effective resource management, decision-making, and polices on human actions to protect wildlife and natural resources. Specifically, the project aims to: (1) explore a citizen scientist-based approach and system for large-scale sustainable data collection; (2) develop deep-learning based fine-grain animal species recognition from large data sets and automated content annotation; (3) study cloud-based computing with thin-client access and resource allocation for scalable deployment and easy access by citizen scientists; and (4) develop a comprehensive data annotation quality monitoring and control framework with tightly coupled computer annotation, crowd-sourcing, and expert review to ensure a high scientific standard of data quality. These tools will be integrated into the eMammal infrastructure to study three questions on wildlife sustainability: energy development, housing development, and wildlife harvest. These tools will also be available to other wildlife researchers using the eMammal system, enabling an improved understanding how humans can live sustainably with wildlife. Collaborative wildlife monitoring and tracking at large geographical and time scales will contribute to the understanding of complex dynamics of wildlife systems, and provide important scientific evidence for informed decisions and effective solutions to sustainability issues in wildlife environments. This project will provide unique, exciting, and interdisciplinary opportunities for mentoring graduate students and involving K-12 and undergraduate students into professionally guided research. The citizen science approach used in this project should accommodate hundreds of students in research.