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

New York University, New York NY

Investigators

Abstract

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.