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

每年，洪水，飓风和野火造成超过1250亿美元的损失和生命损失。不幸的是，德克萨斯州也不例外，它是联邦政府宣布的年度灾害最多的州，自1980年以来经济损失超过1000亿美元。BIPOC和低收入社区通常受到的影响最大。 低成本（1 - 1.2万美元）的无人驾驶航空系统（无人机）和无人驾驶海洋水面车辆（机器人船），加上人工智能和地理空间软件的进步，可以彻底改变社区如何准备，预防和最大限度地减少损失。然而，德克萨斯州的应急管理人员目前缺乏劳动力和知识，无法以有意义的方式调查和实施这些技术。灾害科学的进展缓慢，部分原因是研究人员无法获得全面的纵向数据集，无法将计算机视觉/机器学习（CV/ML）应用于最紧迫的需求。这个为期一年，38.4万美元的试点项目在德克萨斯州A M研究所的指导下，将在三个脆弱的社区建立一个可持续的以研究为中心的公民参与周期：农村（布莱恩），城市（休斯顿），沿海（加尔维斯顿）。应急管理人员将与研究和发展伙伴合作，每年确定紧迫的需求。预计约有90名学生将以某种形式与五个应急管理机构，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合作。这些学生来自76%经济困难、23%非裔美国人和57%西班牙裔的学校，他们将接受培训，收集或处理灾前减灾数据。这些活动将扩大他们的STEM和职业证书课程，机器人俱乐部和孵化器的经验。这些数据和数据产品将立即提供给州和地方灾前减灾机构。第一年的数据可以通过为共同的规划决策提供信息，例如保护开放空间和购买脆弱的住房，从而为每个包裹节省大约2.1万美元。 研究部分将通过提供可以回答六个基础研究问题的数据，为灾害科学、机器人技术、人工智能和城市土地利用规划的根本进步做出贡献。它将创建最大的综合性纵向无人驾驶车辆图像数据集，用于灾前减灾。这些数据集将建立CV/ML在灾害科学中的可信度，开发新的算法来识别不同季节和天气条件下的脆弱性，并进一步加深对迁移学习的基本理解。性能数据将导致一个基于信息学的采样模型，该模型捕获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CIVIC创新挑战赛的一部分，该挑战赛由NSF、能源汽车技术办公室、国土安全部科学技术局和联邦应急管理局合作。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。

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.