RAPID: Collaborative Research: Data Mining and Fusion Between Unmanned Aerial Systems and Social Media Technologies to Improve Emergency Operations

RAPID：协作研究：无人机系统和社交媒体技术之间的数据挖掘和融合，以改善紧急行动

• 批准号: 1945787
• 负责人: Navid Jafari
• 金额: $ 9万
• 依托单位: Louisiana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1945787&HistoricalAwards=false
• 关键词: RAPID; Collaborative; Research; Data; Mining

Disasters such as Hurricane Barry, Hurricane Harvey and others provide an opportunity to document how emergency operation centers (EOCs) can use technology to respond to a natural disaster by understanding and mitigating the uncertainties involved. The use of social media, Unmanned Autonomous Systems/Unmanned Aerial Vehicles (UAVs), real-time disaster modeling, and widespread connectedness means more efficient analysis and flow of information. Immediate information on the location of most damaged areas of a city or stranded people will save lives. Real-time data allows emergency management to develop more targeted response and post disaster recovery plans, and this is regarded as a technological leap from the previous search and rescue strategies attempted decades ago. The proposed research plan formulated by team will not only collect relevant information from real hazard events, but will also analyze and integrate the data to develop the post disaster management frameworks using advanced technologies including UAVs, and various other modes of data collection. Documenting the current operational inefficiencies, technology gaps, and data analysis limitations of EOCs are important for improvements in disaster preparedness and response. Furthermore, this project has time urgency due to the need to collect and utilize time sensitive data from this recent set of storms in the construction of the framework.This RAPID project will leverage Hurricane Barry as a mechanism for creation of a framework that will be integrated into Emergency Operations Centers (EOCs) to support post-disaster analysis and decision-making. Hurricane Barry was accompanied by extensive flooding in coastal Louisiana communities and this has provided a perishable and voluminous data set of social media and UAV imagery for analysis. The project will develop tools for data mining of the social media and fusion with collected UAV imagery for post-disaster analysis. As part of this project, feedback from EOC operators and decision-makers will be provided that will enable enhancement of algorithms and analyses to support recovery as well as response to social media generated rumors. Data from Beaumont EOC from Hurricane Harvey, which also includes data on the recovery of the community will also be collected. By combining data from both regions, a richer dataset will be produced to make comparative analyses and linkages across space, time, disaster level, and socioeconomic factors. This RAPID project will culminate with collecting and archiving (in the National Science Foundation's Natural Hazards Engineering Research Infrastructure DesignSafe) a rich dataset of EOC operations and technology application from Hurricanes Harvey and Barry. However, it is vital that the knowledge gained be reciprocated back to the EOCs so that they can make improvements for the betterment of US citizens. Research team will develop a manual documenting lessons learned for EOC in Beaumont and Louisiana and how to adopt and implement technology. Technology adoption and implementation requires active learning to retain knowledge. Thus, one-day short courses will be developed that will provide UAV and Twitter demonstrations along with examples of data analysis and fusion.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.

飓风巴里、飓风哈维等灾害提供了一个机会，记录紧急行动中心 (EOC) 如何利用技术通过了解和减轻所涉及的不确定性来应对自然灾害。社交媒体、无人驾驶系统/无人机 (UAV)、实时灾害建模和广泛的互联性的使用意味着更有效的分析和信息流动。有关城市受损最严重地区或滞留人员位置的即时信息将挽救生命。实时数据使应急管理部门能够制定更有针对性的响应和灾后恢复计划，这被认为是相对于几十年前尝试的搜救策略的技术飞跃。团队制定的研究计划不仅会从真实灾害事件中收集相关信息，还将利用无人机等先进技术和各种其他数据收集模式对数据进行分析和整合，以制定灾后管理框架。记录 EOC 当前的运营效率低下、技术差距和数据分析限制对于改进备灾和响应非常重要。此外，由于在构建框架时需要收集和利用最近这组风暴的时间敏感数据，因此该项目具有时间紧迫性。该 RAPID 项目将利用飓风巴里作为创建集成框架的机制进入紧急行动中心（EOC）以支持灾后分析和决策。飓风巴里伴随着路易斯安那州沿海社区的大面积洪水，这提供了用于分析的易腐烂且大量的社交媒体和无人机图像数据集。该项目将开发社交媒体数据挖掘工具，并与收集的无人机图像融合以进行灾后分析。作为该项目的一部分，将提供来自 EOC 运营商和决策者的反馈，这将有助于增强算法和分析，以支持恢复以及对社交媒体产生的谣言的回应。博蒙特 EOC 飓风哈维的数据也将被收集，其中还包括社区恢复的数据。通过结合两个地区的数据，将产生更丰富的数据集，以进行空间、时间、灾害程度和社会经济因素的比较分析和联系。该 RAPID 项目最终将收集和归档（在国家科学基金会的自然灾害工程研究基础设施 DesignSafe 中）来自飓风哈维和巴里的 EOC 操作和技术应用的丰富数据集。然而，至关重要的是，将获得的知识回报给平等机会委员会，以便他们能够做出改进，造福美国公民。研究团队将编写一本手册，记录博蒙特和路易斯安那州 EOC 的经验教训以及如何采用和实施技术。技术的采用和实施需要主动学习来保留知识。因此，将开发为期一天的短期课程，提供无人机和 Twitter 演示以及数据分析和融合的示例。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查进行评估，被认为值得支持标准。