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

Cyber​​SEES：类型 2：协作研究：支持大规模野生动物监测以及野生动物和生态可持续性研究的网络基础设施和技术

• 批准号: 1539389
• 负责人: Zhihai He
• 金额: $ 69.95万
• 依托单位: University of Missouri-Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-11-01 至 2020-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1539389&HistoricalAwards=false
• 关键词: CyberSEES; Type; Collaborative; Research; Cyber

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.

最大限度地减少人类活动对野生动物的影响是保护生物学的优先事项。 分布式运动敏感相机，或相机陷阱，是监测野生动物种群的常用工具。 最近的工作表明，相机陷阱调查可以通过公民科学的众包扩大到大规模，产生评估可持续发展战略对野生动物种群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，这给数据处理和质量控制带来了新的挑战。该项目旨在开发先进的计算技术，用于基于云的大规模数据感测，分析，注释，管理和保护野生动物和生态可持续性，为有效的资源管理，决策和人类行动提供信息，以保护野生动物和自然资源。具体来说，该项目旨在：（1）探索基于公民科学家的大规模可持续数据收集方法和系统;（2）开发基于深度学习的大数据集细粒动物物种识别和自动化内容注释;（3）研究基于云的计算，具有瘦客户端访问和资源分配，以实现可扩展部署并方便公民科学家访问;及（4）发展一个全面的数据注释质量监控框架，结合紧密耦合的计算机注释、众包和专家评审，以确保高科学标准的数据质量。这些工具将被集成到eMammal基础设施中，以研究野生动物可持续性的三个问题：能源开发，住房开发和野生动物收获。 这些工具也将提供给使用eMammal系统的其他野生动物研究人员，使他们能够更好地了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合作监测和跟踪野生动物将有助于了解野生动物系统的复杂动态，并为野生动物环境中可持续性问题的知情决策和有效解决方案提供重要的科学证据。该项目将提供独特的，令人兴奋的，跨学科的机会，指导研究生，并涉及K-12和本科生进入专业指导的研究。 在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的研究。