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

数字图像和视频已经成为野生动物研究最普遍和最便宜的数据源，特别是当精心设计的科学努力可以与公众合作来扩大覆盖范围和数据量时。从种群估计到绘制迁徙路线和复杂的社会网络，收集的个体动物图像可以促进统计建模、计算机视觉的人工智能和保护生物学的新发现，并为有效的资源管理政策提供数据驱动的基础。开源的Wild Me生态系统(https://github.com/wildmeorg))帮助大学和当地非政府组织的野生动物研究人员管理大量的这种可视数据，使用多级机器学习管道来发现、计数，甚至在照片中单独识别野生动物，以支持种群生物学、社会生态学等。该项目的软件支持的数据和用例促进了大学计算机科学和应用人工智能的教育，使用令人信服的、真实的野生动物数据来挑战学生。通过开源社区建设，该项目可以扩展其物种覆盖范围和跨学科研究影响，为教育和野生动物研究提供先进的基础。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增长努力(即吸引软件专业人员和科学家)。管理组织将建立代码、人工智能模型和相关数据的基础贡献模型，并定义Wild Me生态系统的社区治理模型。该项目的努力还包括开发培训材料以鼓励开放源码贡献，接触相关的专业和学术用户(潜在的代码贡献者)，以及定义学术界和实地生物学家之间的数据流和机器学习模型，以进行跨学科合作。该项目首先确定我们现有用户社区中现有的、未得到满足的需求和开源需求。然后，它将创建转换为所需服务的差距分析，并提供代码示例和文档，以使第三方贡献变得容易和安全。该项目将为代码和范本的贡献和审查建立质量和安全标准，并为社区讨论和贡献制定行为守则。该项目最终为蓬勃发展的开源生态系统奠定了基础，该生态系统支持多学科、协作的野生生物生物学和生态学，使用人工智能作为扩大和加快每一项研究工作的有效工具。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.