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

RII Track-2 FEC：使用机器学习方法解释的航空图像优化自然资源供应链

• 批准号: 2119689
• 负责人: Jesse Johnson
• 金额: $ 391.41万
• 依托单位: University of Montana
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-15 至 2026-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2119689&HistoricalAwards=false
• 关键词: RII; Track; FEC; Natural; Resource

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.

蒙大拿大学和阿拉斯加大学安克雷奇分校将进行科学研究，以满足其自然资源经济的需求。具体来说，雪水资源，荒地火灾管理和废弃油井监测领域的问题将推动研究议程。科学工作由一套回答问题的通用技术结合在一起。此外，常见的方法是：1）使用称为无人机的无人驾驶飞行器来收集图片和其他测量数据。无人机获取的信息虽然质量很高，但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。我们的活动通过与当地公司的合作伙伴关系和参与学生的实习与职业培训保持一致。由于强调实习，重点将是留住学生，而不是招聘学生。 多样性和包容性的努力将与劳动力发展相结合，以确保我们管辖范围内的土著，低收入和农村成员是努力的组成部分。建议使用机器学习（ML）来处理由自主航空系统（UAS）获取的图像和其他数据。经过处理的数据将通过提供明确的假设检验手段，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，农业产量和经济增长需要改进对山区积雪中储存的自然资本的评估。2)森林火灾管理和科学，因为需要对引发野火的物理和生态过程有深入的了解，以便更好地保护森林及其关键基础设施。 3)废弃油井监测，因为检测和绘制未封盖或密封不当的石油和天然气井将为改善缓解、现场复垦和消除危险提供关键数据。威尔斯。该团队将蒙大拿大学和阿拉斯加大学安克雷奇分校聚集在一起，进行当地相关的研究。当地的重点加强了与附近的商业利益的关系，以确定在现代自然资源为基础的经济中的学术和商业努力共同的问题。UAS和ML是商业和学术工作中至关重要的技能。为了推动辖区内以自然资源为基础的先进制造业，我们特别考虑培养从两年制大学毕业生到初级教师的劳动力。该奖项的计划集中在与主要合作伙伴的带薪、有学分的实习，将商业利益引入拟议的工作，并将初级教师带到研究中。多样性和包容性的努力将与劳动力发展相结合，以确保管辖区的土著，低收入和农村成员是努力的一个组成部分。由于强调实习，重点将是留住学生，而不是招聘学生。该项目涉及对工人产生深远影响的颠覆性技术。为了解决与这些技术相关的后果，该团队寻求社会科学家的帮助，进行评估社会和经济影响的研究。该奖项反映了NSF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。

项目成果

Application of LiDAR Derived Fuel Cells to Wildfire Modeling at Laboratory Scale

LiDAR 衍生燃料电池在实验室规模野火建模中的应用

• DOI: 10.3390/fire6100394
• 发表时间: 2023
• 期刊: Fire
• 作者: Marcozzi, Anthony A.;Johnson, Jesse V.;Parsons, Russell A.;Flanary, Sarah J.;Seielstad, Carl A.;Downs, Jacob Z.
• 通讯作者: Downs, Jacob Z.

Cold Season Rain Event Has Impact on Greenland's Firn Layer Comparable to Entire Summer Melt Season

冷季降雨事件对格陵兰岛冷杉层的影响相当于整个夏季融化季节

• DOI: 10.1029/2023gl103654
• 发表时间: 2023
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: Harper, J.;Saito, J.;Humphrey, N.
• 通讯作者: Humphrey, N.