SCC-CIVIC-FA Track B: Community-Centric Pre-Disaster Mitigation with Unmanned Aerial and Marine Systems

SCC-CIVIC-FA 轨道 B：利用无人机和海洋系统进行以社区为中心的灾前减灾

• 批准号: 2133297
• 负责人: Robin Murphy
• 金额: $ 38.36万
• 依托单位: Texas A&M Engineering Experiment Station
• 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: 2021-10-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2133297&HistoricalAwards=false
• 关键词: SCC; CIVIC; FA; Track; Community

Each year, floods, hurricanes, and wildfires result in over $125 billions of dollars of losses and loss of life. Unfortunately, Texas, the state with the greatest number of annual federally declared disasters, and over $100B in economic losses since 1980, is no exception. Often BIPOC and low-income communities are impacted the most. Low-cost ($1-12K) unmanned aerial systems (drones) and unmanned marine surface vehicles (robot boats), coupled with advances in artificial intelligence and geospatial software, could revolutionize how communities prepare, prevent, and minimize losses. However, Texas emergency managers currently lack the workforce and knowledge to investigate and implement these technologies in a meaningful way. Advances in disaster science are slow in part because researchers do not have access to comprehensive, longitudinal datasets to apply computer vision/machine learning (CV/ML) to the most pressing needs. This one-year, $384K pilot program under the direction of the Texas A&M Institute for a Disaster Resilient Texas will create a sustainable research-centric civic engagement cycle in three vulnerable communities: rural (Bryan), urban (Houston), coastal (Galveston). Emergency managers, working with research and development partners, will annually determine pressing needs. Approximately 90 students are expected to work in some form with five emergency management agencies, five universities including CMU and UC Berkeley, three companies, and two non-profits. The students, taken from the schools where 76% are economically disadvantaged, 23% African-American, and 57% Hispanic, will be trained to collect or process pre-disaster mitigation data. These activities will amplify their STEM and career certificate courses, robotics clubs, and incubator experiences. The data and data products will be immediately available to state and local pre-disaster mitigation agencies. Data in the first year can result in savings on the order of $21K per parcel by informing common planning decisions, such as protecting open space and buying out vulnerable housing. The research component will contribute to fundamental advances in disaster science, robotics, AI, and urban land use planning by providing access to data that can answer six fundamental research questions. It will create the largest comprehensive, longitudinal datasets of unmanned vehicle imagery for pre-disaster mitigation. The datasets will establish the trustworthiness of CV/ML for disaster science, develop new algorithms for recognition of vulnerabilities during different seasons and weather conditions, and further the fundamental understanding of transfer learning. Performance data will lead to an informatics-based model of sampling that captures the technical tradeoffs between accuracy, resolution, and frequency on identifying objects and scene understanding. This project is part of the CIVIC Innovation Challenge which is a collaboration of NSF, Department of Energy Vehicle Technology Office, Department of Homeland Security Science and Technology Directorate and Federal Emergency Management Agency.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.

每年，洪水，飓风和野火都会造成超过1,250亿美元的损失和生命损失。不幸的是，德克萨斯州是年度联邦宣布灾难数量最多的州，自1980年以来的经济损失超过100B美元，也不例外。通常对BIPOC和低收入社区受到影响最大。 低成本（$ 1-12k）无人驾驶系统（无人机）和无人海洋表面车辆（机器人船），再加上人工智能和地理空间软件的进步，可以彻底改变社区的准备，预防和最小化损失的方式。但是，德克萨斯州应急管理人员目前缺乏以有意义的方式调查和实施这些技术的劳动力和知识。灾难科学的进步很慢，部分原因是研究人员无法访问全面的纵向数据集来将计算机视觉/机器学习（CV/ML）应用于最紧迫的需求。这项为期一年的38.4万美元的试点计划在得克萨斯州A＆M灾难研究所的指导下，得克萨斯州的灾难弹性研究所将在三个脆弱的社区中创建一个以研究为中心的以研究为中心的公民参与周期：农村（Bryan），Urban（Urban），Urban（Houston），沿海地区（Galveston）。与研发合作伙伴合作的应急管理人员每年将确定紧迫的需求。预计大约有90名学生将以五个紧急管理机构，包括CMU和加州大学伯克利分校在内的五所大学，三家公司和两家非营利组织的形式工作。这些学生从经济上处于不利地位的76％的学校，23％的非裔美国人和57％的西班牙裔学校，他们将接受培训，以收集或处理污点前缓解数据。这些活动将扩大其STEM和职业证书课程，机器人俱乐部和孵化器体验。数据和数据产品将立即适用于州和地方前缓解机构。第一年的数据可以通过告知共同的计划决策，例如保护开放空间和购买脆弱的住房，从而节省每个包裹的订单。 该研究组成部分将通过提供可以回答六个基本研究问题的数据，从而有助于灾难科学，机器人技术，AI和城市土地利用计划的基本进步。它将创建最大的无人车辆图像的全面，纵向数据集，以减轻降水前。数据集将建立简历/ML对灾难科学的可信赖性，开发新算法以识别不同季节和天气状况的脆弱性，并进一步对转移学习的基本了解。性能数据将导致基于信息学的采样模型，该模型捕获了识别对象和场景理解的准确性，分辨率和频率之间的技术权衡。该项目是公民创新挑战赛的一部分，该挑战是NSF的合作，能源车辆技术局，国土安全科学技术局和联邦紧急事务管理局和联邦紧急事务管理机构。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来通过评估来支持的。