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: 1539622
• 负责人: Roland Kays
• 金额: $ 31万
• 依托单位: Friends of the North Carolina State Museum of Natural Sciences
• 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 2019-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1539622&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行为对野生动物的影响是保护生物学的首要任务。 分布式运动敏感摄像机或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。该项目旨在开发先进的计算技术，用于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，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政策提供信息。具体而言，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度动物物种识别和自动内容注释； (3) 研究基于云计算的瘦客户端访问和资源分配，以实现可扩展部署和公民科学家的轻松访问； （4）开发一个全面的数据标注质量监控框架，将计算机标注、众包和专家评审紧密耦合，以确保数据质量的高科学标准。这些工具将集成到 eMammal 基础设施中，以研究有关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。 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，从而更好地了解人类如何与野生动物可持续共存。大地理和时间尺度上的协作野生动物监测和跟踪将有助于了解野生动物系统的复杂动态，并为野生动物环境可持续性问题的明智决策和有效解决方案提供重要的科学证据。该项目将为指导研究生以及让 K-12 和本科生参与专业指导的研究提供独特、令人兴奋的跨学科机会。 该项目中使用的公民科学方法应能容纳数百名学生进行研究。