Reconstruction of Four-Dimensional Near-Surface Wind Characteristics from Debris and Damage Attributes using Computer Vision

利用计算机视觉从碎片和损伤属性重建四维近地表风特性

• 批准号: 2053935
• 负责人: David Roueche
• 金额: $ 39.92万
• 依托单位: Auburn University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-15 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2053935&HistoricalAwards=false
• 关键词: Reconstruction; Four; Dimensional; Near; Surface

Extreme windstorms, including hurricanes, tornadoes, and thunderstorms, are major drivers of economic losses and fatalities in the United States. Mitigating these impacts requires in-depth understanding of the fundamental characteristics of extreme windstorms. These characteristics then inform building codes and standards, education, and risk assessment. Significant knowledge gaps exist for tornadoes and thunderstorms, with basic characteristics such as near-ground wind speeds rarely measured directly. A promising new approach to address these gaps is the application of computer vision techniques to track the 4D motion of wind-borne debris that is contained in the numerous videos of extreme windstorms generated each year by scientists and citizen scientists. This multi-disciplinary Disaster Resilience Research Grants (DRRG) project will integrate wind engineering, structural engineering, computer vision, and machine learning disciplines to develop robust new datasets and methods for understanding near-surface wind and debris characteristics. Graduate students from each discipline will be trained in cross-disciplinary methods. The engagement of citizen scientists will spur awareness and education of the public as to the true nature of these windstorms. Ultimately, the improved understanding of near-ground level winds and debris in extreme windstorms addresses the critical need for improved community resilience to extreme windstorms. Little is known about the near-surface characteristics of extreme windstorms and the debris they generate. High space and time resolution of velocity fields of these storms are rarely measured in-situ, resulting in fundamental characteristics such as the relative magnitudes of the horizontal and vertical velocity components, vertical profiles of the 3D velocities, and turbulence intensities remaining largely unknown. This project adopts an innovative and integrated approach to characterizing near-surface wind and debris characteristics using visual data sources. The primary objectives of this project are to (1) build a formal database of both structured and unstructured debris motion media with appropriate metadata; (2) generate a robust dataset of labeled debris motion suitable for model training and validation; (3) develop a new generation of computer vision and machine learning based tools with application to fine-scale and large-scale debris identification, classification, and motion tracking; and (4) demonstrate a framework for inferring near-surface wind characteristics from debris motion. In fulfilling these objectives, this project will utilize collaborations with the NHERI Wall of Wind Experimental Facility at Florida International University and citizen scientists within storm chasing networks. The datasets created through this project can be used to train a new generation of tools, integrating artificial intelligence and civil engineering in ways that will ultimately benefit both fields. The outcome will be a deeper understanding of extreme windstorms, with a framework in place for continuous refinement and learning beyond the lifespan of this project.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.

极端风暴，包括飓风、龙卷风和雷暴，是造成美国经济损失和死亡的主要原因。减轻这些影响需要深入了解极端风暴的基本特征。这些特征为建筑规范和标准、教育和风险评估提供了信息。关于龙卷风和雷暴存在着巨大的知识缺口，近地风速等基本特征很少直接测量。解决这些差距的一个有希望的新方法是应用计算机视觉技术来跟踪每年由科学家和公民科学家生成的大量极端风暴视频中包含的风载碎片的4D运动。这个多学科抗灾研究资助（DRRG）项目将整合风工程、结构工程、计算机视觉和机器学习学科，开发强大的新数据集和方法，以了解近地面风和碎片特征。每个学科的研究生将接受跨学科方法的培训。公民科学家的参与将促进公众对这些风暴的真实性质的认识和教育。最终，提高对极端风暴中近地面风和碎片的理解，解决了提高社区对极端风暴恢复能力的关键需求。人们对极端风暴的近地表特征及其产生的碎片知之甚少。这些风暴的高空间和时间分辨率的速度场很少在现场测量，导致基本特征，如水平和垂直速度分量的相对大小，三维速度的垂直剖面和湍流强度在很大程度上仍然未知。该项目采用了一种创新的综合方法，利用可视化数据源来表征近地面风和碎片的特征。该项目的主要目标是：(1)建立一个具有适当元数据的结构化和非结构化碎片运动介质的正式数据库；(2)生成适合于模型训练和验证的标记碎片运动鲁棒数据集；(3)开发新一代基于计算机视觉和机器学习的工具，应用于精细尺度和大规模碎片识别、分类和运动跟踪；(4)展示了一个从碎屑运动推断近地面风特征的框架。为了实现这些目标，该项目将利用与佛罗里达国际大学NHERI风力实验设施和风暴追逐网络中的公民科学家的合作。通过该项目创建的数据集可用于培训新一代工具，以最终使两个领域受益的方式整合人工智能和土木工程。其结果将是对极端风暴有更深入的了解，并建立一个框架，以便在项目生命周期之后继续改进和学习。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。