POSE: Phase I: Wildbook: Building an Open Source Community for AI-Enabled Wildlife Science and Computer Science Education

POSE：第一阶段：Wildbook：为人工智能野生动物科学和计算机科学教育构建开源社区

• 批准号: 2229782
• 负责人: Jason Holmberg
• 金额: $ 29.7万
• 依托单位: WILD ME
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-15 至 2024-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229782&HistoricalAwards=false
• 关键词: POSE; Phase; Wildbook; Building; Open

Digital images and video have become the most ubiquitous and inexpensive data sources for wildlife research, especially when well designed scientific efforts can partner with the public to increase the breadth of coverage and volume of data. From population estimation to mapping migration routes and complex social networks, collected imagery of individual animals can foster new discoveries in statistical modeling, AI for computer vision, and conservation biology, as well as provide a data-driven basis for effective resource management policy. The open source Wild Me ecosystem (https://github.com/wildmeorg) aids wildlife researchers at universities and local NGOs in curating large volumes of this visual data, employing a multistage machine learning pipeline to find, count, and even individually identify wildlife in photographs in support of population biology, social ecology, and more. The project’s software-supported data and use cases have advanced university education in computer science and applied AI using compelling, real world data of wildlife to challenge students. Through open source community building, the project can scalably grow its species coverage and its interdisciplinary research impact, providing an advanced foundation for education and wildlife research.This project will advance the Wild Me ecosystem through building an open source managing organization, which is responsible for the initial open source community growth effort (i.e. attracting software professionals and scientists). The managing organization will establish the foundational contribution model for code, AI models, and related data and define the community governance model for the Wild Me ecosystem. The project’s efforts also include developing training materials to encourage open source code contribution, reaching out to related professional and academic users (potential code contributors), and defining the flow of data and machine learning models back and forth between academia and field biologists for interdisciplinary collaboration. The project begins with identifying existing, unmet needs and open source appetite in our existing user community. It then will create a gap analysis translated into needed services and provide code examples and documentation to make it easy and secure for third party contributions. The project will establish quality and security standards for code and model contribution and review, as well as set a code of conduct for community discussion and contribution. The project ultimately establishes the foundation for a thriving and growing open source ecosystem that supports multidisciplinary, collaborative wildlife biology and ecology using AI as an effective tool to scale and speed each research effort.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

数字图像和视频已成为野生动物研究中最普遍且廉价的数据源，特别是当精心设计的科学工作可以与公众合作以增加从种群估计到绘制迁徙路线和复杂的社交网络的覆盖范围和数据量时。收集的个体动物图像可以促进统计模型、计算机视觉人工智能和保护生物学方面的新发现，并为有效的资源管理政策提供数据驱动的基础。 .com/wildmeorg）帮助野生动物研究人员大学和当地非政府组织整理了大量的视觉数据，采用多级机器学习管道来查找、计数甚至单独识别照片中的野生动物，以支持种群生物学、社会生态学等。用例具有先进的计算机科学大学教育和应用人工智能，使用野生动物的真实世界数据来挑战学生，通过开源社区建设，该项目可以扩大其物种覆盖范围和跨学科研究影响，为教育提供先进的基础。和野生动物研究。该项目将通过建立一个开源管理组织来推进 Wild Me 生态系统，该组织负责最初的开源社区发展工作（即吸引软件专业人员和科学家）。该管理组织将为代码、人工智能模型和相关内容建立基础贡献模型。该项目的工作还包括开发培训材料以鼓励开源代码贡献、接触相关专业和学术用户（潜在的代码贡献者）以及定义数据和机器的流动。学术界之间来回学习模型该项目首先确定我们现有用户社区中现有的、未满足的需求和开源需求，然后将差距分析转化为所需的服务，并提供代码示例和文档，以使其变得简单和安全。该项目将为代码和模型贡献和审查制定质量和安全标准，并制定社区讨论和贡献的行为准则。该项目最终为支持蓬勃发展的开源生态系统奠定了基础。使用人工智能作为有效的多学科、协作野生动物生物学和生态学该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。