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

美国-爱尔兰伙伴计划：Urban ARK：城市地区洪水管理的评估、风险管理和知识

• 批准号: 1826134
• 负责人: Debra Laefer
• 金额: $ 32.59万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1826134&HistoricalAwards=false
• 关键词: US; Ireland; Partnership; Program; Urban

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.

当前城市地区的洪水模型在描述洪水发生时和恢复过程中洪水对人类和财产的影响时，经常未能包括地下空间的存在，例如地下交通网络、住宅和办公室地下室以及地下停车场。该项目开发新的工具，利用遥感，分布式计算和可视化的最新进展，以支持更准确的洪水图的创建。 该项目将生成一种集成激光扫描数据和洪水预测模型的移动的、易于部署的、低成本的沉浸式洪水风险通信工具。 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水相关问题进行接触，例如因施工或社区活动而造成的临时出入限制，或协助那些行动受限的人。 NSF将资助在纽约市工作的美国研究人员，利用在爱尔兰都柏林和北爱尔兰贝尔法斯特工作的爱尔兰和北爱尔兰研究人员（由爱尔兰和北爱尔兰资助）的协调努力。 这一科学研究贡献因此支持NSF的使命，以促进科学的进步，并提高我们的国家福利的好处，可以告知决策者参与恢复城市地区。该项目解决洪水风险评估生成和风险沟通的背景下，3个研究领域：纽约市在美国，都柏林在爱尔兰共和国，和贝尔法斯特在北爱尔兰爱尔兰。照片，航空激光扫描和高光谱图像被视为输入流，探索模型构建和风险沟通的几个范例。这些输入流来源于现有的数据收集，并通过使用SLAM（同步定位和映射）技术的机会性数据收集，用于街道和地下空间。利用这些数据，将开发技术来确定和估计城市环境中地下空间的大小。数据将存储在基于Map-Reduce的新形式的综合空间数据库中，并托管在云中。该数据库将在公共和私有云平台上进行评估，以确保其完全可扩展，并与洪水数值模型互操作。该数据库将通过图形用户界面进入，使用户能够在不同的空间和时间粒度级别查询数据，并提取所得数据，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，该项目还旨在了解洪水条件下的风险意识和过去的行为，以告知新的风险沟通工具。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且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。

项目成果

Metrics for aerial, urban lidar point clouds

空中、城市激光雷达点云的指标

• DOI: 10.1016/j.isprsjprs.2021.01.010
• 发表时间: 2021
• 期刊: ISPRS Journal of Photogrammetry and Remote Sensing
• 影响因子: 12.7
• 作者: Stanley, Michael H.;Laefer, Debra F.
• 通讯作者: Laefer, Debra F.

THE CASE FOR LOW-COST, PERSONALIZED VISUALIZATION FOR ENHANCING NATURAL HAZARD PREPAREDNESS

低成本、个性化可视化增强自然灾害防备的案例

• DOI: 10.5194/isprs-archives-xliv-m-2-2020-37-2020
• 发表时间: 2020
• 期刊: Remote Sensing and Spatial Information Sciences
• 作者: Gmelch, P.;Lejano, R.;O’Keeffe, E.;Laefer, D. F.;Drell, C.;Bertolotto, M.;Ofterdinger, U.;McKinley, J.
• 通讯作者: McKinley, J.

Efficient LiDAR point cloud data encoding for scalable data management within the Hadoop eco-system

高效的 LiDAR 点云数据编码，用于 Hadoop 生态系统内的可扩展数据管理

• DOI: 10.1109/bigdata47090.2019.9006044
• 发表时间: 2020
• 期刊: 2019 IEEE International Conference on Big Data
• 作者: Vo, A.V.;Hewage, C.N.L.;Russo, G.;Chauhan, N.;Laefer, D.F.;Bertolotto, M.;Le-Khac, N.A.;Oftendinger, U.
• 通讯作者: Oftendinger, U.

A PARALLEL ALGORITHM FOR LOCAL POINT DENSITY INDEX COMPUTATION OF LARGE POINT CLOUDS

• DOI: 10.5194/isprs-annals-viii-4-w2-2021-75-2021
• 发表时间: 2021
• 期刊: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• 作者: A. Vo;C. N. Lokugam Hewage;N. A. Le Khac;M. Bertolotto;D. Laefer

A HIGHLY SCALABLE DATA MANAGEMENT SYSTEM FOR POINT CLOUD AND FULL WAVEFORM LIDAR DATA

适用于点云和全波形激光雷达数据的高度可扩展的数据管理系统

• DOI: 10.5194/isprs-archives-xliii-b4-2020-507-2020
• 发表时间: 2020
• 期刊: Remote Sensing and Spatial Information Sciences
• 作者: Vo, A. V.;Laefer, D. F.;Trifkovic, M.;Hewage, C. N.;Bertolotto, M.;Le-Khac, N. A.;Ofterdinger, U.
• 通讯作者: Ofterdinger, U.