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

飓风巴里、飓风哈维和其他灾难提供了一个机会，以记录紧急操作中心(EOC)如何通过了解和减少涉及的不确定性来使用技术来应对自然灾害。使用社交媒体、无人自主系统/无人驾驶飞行器(UAV)、实时灾难建模和广泛的连接意味着更高效的信息分析和流动。关于城市受灾最严重地区或受困人员位置的即时信息将拯救生命。实时数据使应急管理能够制定更有针对性的响应和灾后恢复计划，这被视为与几十年前尝试的以前的搜救策略相比的一次技术飞跃。该团队制定的拟议研究计划不仅将从真实的危险事件中收集相关信息，还将分析和整合这些数据，以利用包括无人机在内的先进技术以及各种其他数据收集模式来开发灾后管理框架。记录EOC目前的业务低效、技术差距和数据分析限制，对于改进备灾和应对非常重要。此外，该项目具有时间紧迫性，因为在框架的建设中需要收集和利用最近一系列风暴的时间敏感数据。这一快速项目将利用飓风巴里作为创建框架的机制，该框架将被整合到紧急行动中心(EOCs)中，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范围洪水，这为分析社交媒体和无人机图像提供了易腐烂的海量数据集。该项目将开发社交媒体的数据挖掘工具，并与收集的无人机图像融合，用于灾后分析。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，以增强算法和分析，以支持恢复以及对社交媒体产生的谣言做出回应。来自博蒙特EOC的飓风哈维的数据也将被收集，其中还包括社区恢复的数据。通过将这两个区域的数据结合起来，将产生一个更丰富的数据集，以便在空间、时间、灾害程度和社会经济因素之间进行比较分析和联系。这个快速项目最终将收集和存档(在国家科学基金会的自然灾害工程研究基础设施设计Safe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，以便他们能够为美国公民的福祉做出改进。研究团队将编写一份手册，记录在博蒙特和路易斯安那州为EOC吸取的经验教训，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以保持知识。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势和更广泛的影响审查标准进行评估，被认为值得支持。

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.