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

极端风暴，包括飓风，龙卷风和雷暴，是美国经济损失和死亡的主要驱动力。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。对龙卷风和雷暴存在着重大的知识空白，其基本特征，如近地风速，很少直接测量。解决这些差距的一种有希望的新方法是应用计算机视觉技术来跟踪风载碎片的4D运动，这些碎片包含在科学家和公民科学家每年生成的大量极端风暴视频中。这个多学科的抗灾研究赠款（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，以开发强大的新数据集和方法来了解近地表风和碎片特征。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正性质的认识和教育。最后，对极端风暴中的近地风和碎片的进一步了解解决了提高社区对极端风暴的复原力的迫切需要。 人们对极端风暴及其产生的碎片的近地表特征知之甚少。这些风暴的速度场的高空间和时间分辨率很少在现场测量，导致基本特征，如水平和垂直速度分量的相对大小，3D速度的垂直分布，湍流强度仍然在很大程度上未知。该项目采用了一种创新的综合办法，利用可视数据源确定近地表风和碎片特征。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）生成一个适合于模型训练和验证的有标记碎片运动的可靠数据集;（3）开发新一代基于计算机视觉和机器学习的工具，用于精细和大规模碎片识别、分类，和运动跟踪;以及（4）演示从碎片运动推断近地面风特征的框架。在实现这些目标的过程中，该项目将利用与佛罗里达国际大学的NHERI风墙实验设施和风暴追踪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训练新一代工具，将人工智能和土木工程以最终使两个领域受益的方式整合在一起。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。

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.