D-ISN/Collaborative Research: Machine Learning to Improve Detection and Traceability of Forest Products using Stable Isotope Ratio Analysis (SIRA)

D-ISN/合作研究：利用稳定同位素比率分析 (SIRA) 提高林产品检测和可追溯性的机器学习

• 批准号: 2240402
• 负责人: Naren Ramakrishnan
• 金额: $ 62.58万
• 依托单位: Virginia Polytechnic Institute and State University
• 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: 2023-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2240402&HistoricalAwards=false
• 关键词: ISN; Collaborative; Research; Machine; Learning

The objective of this Disrupting Operations of Illicit Supply Networks (D-ISN) project is to develop new machine learning approaches to help discover and trace illicitly sourced timber products. Specifically, this project leverages Stable Isotope Ratio Analysis (SIRA), a technique that uses the ratios of several elemental stable isotopes within natural products to help trace their geographic origin. By comparing isotope ratios against reference databases built from verified locations, the researchers can impute the origin of suspicious timber products. The project brings together data scientists, analytical chemists, geospatial and remote sensing specialists, and international trade and supply chain experts to develop new data science approaches that will enhance SIRA accuracy and resolution. This project will advance our national ability to counter nefarious and illegal activities by rapidly imputing the source for timber products, helping identify violators of international treaties and regulations, and thus combat natural resource trafficking.The project will develop new machine learning methods to overcome the relative scarcity of labeled data (compared to traditional machine learning applications like computer vision and natural language processing, where datasets might contain millions of labeled examples). Specifically, the PIs will investigate contrastive learning, generative learning, and science-guided machine learning algorithms that can harness prior domain knowledge to combine climate layers with the best available local-scale data, to ensure fidelity to both large-scale patterns and site-specific observations. In addition to location determination from isotope ratios, the project will develop active sampling strategies to “close the loop”, i.e., quantify a model’s uncertainty and determine future sampling regions in order to improve prediction accuracy and resolution. This project is expected to improve geospatial prediction accuracy of product origin and will enable a cost-benefit analysis to minimize future data collection costs and optimize prediction gain. The project will involve partners in industry, non-profit, and government to source samples and to communicate results with relevant enforcement agencies and SIRA analysis labs. The project will also support graduate students who will be exposed to a multi-disciplinary approach to address important societal problems.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.

该项目的目标是开发新的机器学习方法，以帮助发现和跟踪非法来源的木材产品。具体而言，该项目利用稳定同位素比分析（SIRA），这是一种使用天然产品中几种元素稳定同位素的比率来帮助追踪其地理来源的技术。通过将同位素比率与从经核实的地点建立的参考数据库进行比较，研究人员可以推断可疑木材产品的来源。该项目汇集了数据科学家，分析化学家，地理空间和遥感专家以及国际贸易和供应链专家，以开发新的数据科学方法，提高SIRA的准确性和分辨率。该项目将通过快速估算木材产品的来源，帮助识别违反国际条约和法规的行为，从而打击自然资源贩运，提高我们打击邪恶和非法活动的国家能力。该项目将开发新的机器学习方法，以克服标记数据的相对稀缺性。（与计算机视觉和自然语言处理等传统机器学习应用相比，数据集可能包含数百万个标记示例）。具体而言，PI将研究对比学习，生成学习和科学指导的机器学习算法，这些算法可以利用先前的领域知识将气候层与最佳的局部尺度数据联合收割机结合起来，以确保对大尺度模式和特定地点观测的保真度。除了根据同位素比率确定位置外，该项目还将制定主动取样战略，以“闭合回路”，即，量化模型的不确定性并确定未来的采样区域，以提高预测准确性和分辨率。预计该项目将提高产品原产地地理空间预测的准确性，并将进行成本效益分析，以尽量减少未来的数据收集成本，优化预测收益。该项目将涉及行业、非营利组织和政府的合作伙伴，以获取样本，并与相关执法机构和SIRA分析实验室交流结果。 该项目还将支持研究生谁将接触到一个多学科的方法来解决重要的社会问题。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。