RII Track-2 FEC: Natural Resource Supply Chain Optimization using Aerial Imagery Interpreted with Machine Learning Methods

蒙大拿大学和阿拉斯加大学安克雷奇分校将开展科学研究，以满足其以自然资源为基础的经济需求。具体来说，来自雪水资源、荒地火灾管理和废弃油井监测等领域的问题将推动研究议程。科学工作是由一套共同的回答问题的技术联合起来的。此外，常见的方法是：1)使用无人驾驶飞行器（称为无人机）收集图片和其他测量数据。无人机获取的信息在高质量的同时，也是大量复杂的数据。2)为了帮助解释数据并解决科学问题，我们使用机器学习方法来训练计算机识别数据中的模式。使用无人机收集数据和使用机器学习是美国未来劳动力的关键技能。我们的活动与职业培训相一致，通过与当地公司的合作和参与学生的实习。在强调实习的情况下，重点将是留住学生，而不是招收学生。多样性和包容性的努力将与劳动力发展相结合，确保我们辖区内的土著、低收入和农村成员成为这些努力的组成部分。建议使用机器学习（ML）来处理自主航空系统（UAS）获取的图像和其他数据。经过处理的数据将提供检验假设的明确方法，从而支持自然资源管理方面的科学研究。应用这种方法的自然资源管理的三个领域是：1)雪水资源，因为能源生产、农业产出和经济增长需要改进对山地积雪中储存的自然资本的评估。2)火灾管理和科学，因为对驱动野火的物理和生态过程的深入了解是更好地保护森林及其关键基础设施的管理实践所必需的。3)废弃油井监测，因为探测和测绘未封顶或密封不当的油气井将为改善缓解、场地复垦和消除危害提供关键数据。该团队将蒙大拿大学和阿拉斯加安克雷奇大学联合起来进行当地相关的研究。本地焦点加强了与附近商业利益的关系，以确定现代自然资源经济中学术和商业努力的共同问题。需要注意的是，UAS和ML是商业和学术工作中至关重要的技能。为了促进以自然资源为基础的先进制造业的发展，我们特别考虑发展一支从两年制大学毕业生到初级教师的劳动力队伍。该奖项的计划主要集中在与主要合作伙伴进行带薪、有学分的实习，将商业利益纳入拟议的工作，并将初级教师引入研究。多样性和包容性工作将与劳动力发展相结合，以确保司法管辖区的土著、低收入和农村成员成为工作的组成部分。在强调实习的情况下，重点将是留住学生，而不是招收学生。该项目涉及对工人产生长期影响的颠覆性技术。为了解决与这些技术相关的后果，该团队寻求了一位社会科学家的帮助，进行了评估社会和经济影响的研究。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The University of Montana and the University of Alaska, Anchorage will conduct scientific research that is responsive to the needs of their natural resource based economies. Specifically, questions from the areas of snow water resources, wildland fire management, and abandoned oil well monitoring will drive the research agenda. The scientific work is united by a common set of techniques for answering questions. Moreover, the common approach is to: 1) Use unmanned flying vehicles called drones to collect pictures and other measurements. While the information acquired by drones is high quality, it is also a large amount of complex data. 2) To aid in the data’s interpretation and address the science questions, we use machine learning methods that train computers to identify patterns in data. Collecting data using drones and the use of machine learning are critical skills for America’s future workforce. Our activities are aligned with career training through partnerships with local companies and internships for participating students. With the emphasis on internships, the focus will be retention, rather than recruitment of students. Diversity and inclusion efforts will work in tandem with workforce development to ensure that Indigenous, low income, and rural members of our jurisdictions are integral to the efforts. It is proposed to use machine learning (ML) to process imagery and other data acquired by autonomous aerial systems (UAS). Processed data will support scientific research in natural resource management by providing a clear means of testing hypotheses. The three areas of natural resource management to apply this approach are: 1) snow water resources, because energy production, agricultural output, and economic growth require improved assessment of the natural capital banked in the mountain snowpack. 2) fire management and science, because an advanced understanding of the physical and ecological processes driving wildfire is required for management practices that better protect forests and the critical infrastructure within them. 3) abandoned oil well monitoring, because detecting and mapping uncapped or improperly sealed oil and gas wells will provide critical data for improved mitigation, site reclamation, and hazard removal. The team bring together the University of Montana and the University of Alaska Anchorage to conduct locally relevant research. The local focus strengthens the relations with nearby commercial interests to identify questions common to academic and commercial endeavors in modern natural resource based economies. Attention is called to UAS and ML as skills vital to both commercial and academic work. To advance the natural resource based advanced manufacturing industries of the jurisdictions, special consideration is given to developing a workforce that spans from two-year college graduates to junior faculty members. This award's plan centers on paid, credit-bearing internships with key partners, drawing commercial interests into the proposed work, and bringing junior faculty to the research. Diversity and inclusion efforts will work in tandem with workforce development to ensure that Indigenous, low income, and rural members of the jurisdictions are an integral part of efforts. With the emphasis on internships, the focus will be retention, rather than recruitment of students. The project engages with disruptive technologies that have long reaching consequences for workers. To address the consequences related to these technologies, the team enlisted the aid of a social scientist to conduct studies evaluating the social and economic impacts.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.