US-Ireland Partnership Program: Urban ARK: Assessment, Risk Management, and Knowledge for Flood Management in Urban Areas

目前城市地区的洪水模型往往不包括地下空间--如地下交通网络、住宅和办公地下室以及地下停车场--的存在，无法描述洪水在洪水发生时和恢复期间对人和财产的影响。该项目开发了新的工具，以利用遥感、分布式计算和可视化方面的最新进展，支持创建更准确的洪水地图。该项目将生成一个移动的、易于部署的、低成本的沉浸式洪水风险通信工具，它集成了激光扫描数据和洪水预测模型。这一工具还可用于培训应急工作人员，并与当地社区就需要空间背景的非洪灾相关问题进行接触，例如因施工或社区活动而造成的临时出入限制，或用于协助行动受限的人。NSF将资助在纽约市工作的美国研究人员，利用爱尔兰和北爱尔兰(由爱尔兰和北爱尔兰资助)在爱尔兰都柏林和北爱尔兰贝尔法斯特工作的研究人员的协调努力。因此，这一科学研究贡献支持了NSF的使命，即促进科学进步和提高我们的国家福利，使参与城市地区复原力的政策制定者受益。该项目在3个研究区域--美国的纽约市、爱尔兰共和国的都柏林和北爱尔兰的贝尔法斯特--的背景下解决洪水风险评估、生成和风险沟通。照片、航空激光扫描和高光谱图像被视为输入流，以探索模型建立和风险沟通的几种范例。这些输入流既来自现有的数据收集，也来自利用街道和地下空间的同时定位和测绘(SLAM)技术的机会性数据收集。利用这些数据，将开发出识别和估计城市环境中地下空间大小的技术。数据将存储在一种基于Map-Reduced的新形式的综合空间数据库中，并托管在云中。该数据库将在公共云和私有云平台上进行评估，以确保它完全可扩展，并可与数值洪水模型互操作。该数据库将通过图形用户界面访问，这将允许用户在不同的空间和时间粒度水平上查询数据，并提取结果数据，作为洪水模拟系统的高分辨率输入。除了增强洪水建模能力外，该项目还寻求了解洪水条件下的风险意识和过去的行为，为新的风险沟通工具提供信息。该工具利用项目中开发的高分辨率数据和洪水模型，为公民和其他利益相关者提供低成本和易于部署的虚拟现实体验。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。

Current flood models in urban areas frequently fail to include the presence of subsurface spaces - such as underground transportation networks, residential and office basements, and underground parking - in characterizing the effects of flooding on humans and property at the time of the flood and during recovery. This project develops new tools to exploit recent advances in remote sensing, distributed computing, and visualization to support the creation of more accurate flood maps. This project will generate a mobile, easily deployable, low-cost immersive flood risk communication tool integrating laser scanning data and flood prediction models. This tool can also be adapted for training emergency staff and engagement with local communities about non-flood-related issues requiring spatial context, such as temporary access restrictions due to construction or community events, or for assisting those with mobility restrictions. NSF will fund US-based researchers working in New York City, leveraging the coordinated efforts of researchers in Ireland and Northern Ireland (funded by Ireland and Northern Ireland) working in Dublin, Ireland, and Belfast, Northern Ireland. This scientific research contribution thus supports NSF's mission to promote the progress of science and to advance our national welfare with benefits that can inform policymakers involved in resilience of urban areas.The project addresses flood risk assessment generation and risk communication in the context of 3 study areas; New York City in the United States, Dublin in the Republic of Ireland, and Belfast in Northern Ireland. Photographs, aerial laser scans, and hyperspectral imagery are treated as input streams to explore several paradigms for model building and risk communication. These input streams are sourced both from existing data collections and through opportunistic data collection using SLAM (Simultaneous Localization And Mapping) technology for both street level and underground spaces. Using these data, techniques will be developed to identify and estimate the size of underground spaces in the urban environment. Data will be stored in a new form of integrated spatial database based upon Map-Reduce and hosted in the cloud. The database will be evaluated on both public and private cloud platforms to ensure that it is fully scalable and interoperable with numerical flood models. This database will be accessible through a Graphical User Interface which will allow users to query the data at differing levels of spatial and temporal granularity and extract the resulting data to serve as high resolution input for flood modeling systems. In addition to enhancing flood modeling capabilities, this project seeks to understand risk awareness and past behavior in flood conditions to inform a new risk communication tool. This tool leverages the high-resolution data and flood models developed in the project to provide citizens and other stakeholders with a low-cost and easy to deploy virtual reality experience.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.