United States Department of Transportation
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Street-level Flooding Platform: Sensing and Data Sharing for Urban Accessibility and Resilience [supporting datasets]
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2021-01-08
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[PDF-170.58 KB]
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Alternative Title:NYU FloodSense Gowanus Canal mounted distance sensor, NYU FloodSense street sign mounted distance sensor, NYU FloodSense street sign mounted flood depth sensor, and NYU FloodSense Gowanus canal mounted sensor depth
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Abstract:Access to real-time information on flooding can improve resiliency and efficiency by allowing residents to identify navigable transportation routes and make informed decisions to avoid exposure to floodwater contaminants. However, very little data exist on the frequency and extent of urban surface flooding, and there is an unmet need for hyperlocal information on the presence and depth of street-level floodwater. This unmet need for data from urban floods motivated the development of the FloodSense project in early 2020, with the objective of: developing a platform to provide real-time, street-level flood information - including the presence, frequency, and severity of local surface flood events - to a range of stakeholders, including policy makers, government agencies, citizens, emergency response teams, community advocacy groups, and researchers. The FloodSense project began in 2020 with funding from the C2SMART Transportation Research Center, with overarching goals to (1) design, build, deploy, and assess robust, low-cost sensors in diverse urban environments to track street-level flood occurrence and depth, and ultimately, (2) to implement an interface to communicate the data to a range of stakeholders. This document includes the sections: Hardware development and deployment; Collaborations, stakeholder needs and impact identification; Applications for additional funding; Press; and Next steps.
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Content Notes:Other DOIs associated with the supporting datasets are https://doi.org/10.5281/zenodo.4162295, https://doi.org/10.5281/zenodo.4306915, https://doi.org/10.5281/zenodo.4306918, https://doi.org/10.5281/zenodo.4429273, and https://doi.org/10.5281/zenodo.4429282.
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