Knowledge graphs to support real-time flood impact evaluation

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dc.contributor.authorJohnson, J. Michaelen
dc.contributor.authorNarock, Tomen
dc.contributor.authorSingh-Mohudpur, Justinen
dc.contributor.authorFils, Dougen
dc.contributor.authorClarke, Keith C.en
dc.contributor.authorSaksena, Siddharthen
dc.contributor.authorShepherd, Adamen
dc.contributor.authorArumugam, Sankaren
dc.contributor.authorYeghiazarian, Liliten
dc.date.accessioned2022-07-19T12:46:07Zen
dc.date.available2022-07-19T12:46:07Zen
dc.date.issued2022-03en
dc.description.abstractA digital map of the built environment is useful for a range of economic, emergency response, and urban planning exercises such as helping find places in app driven interfaces, helping emergency managers know what locations might be impacted by a flood or fire, and helping city planners proactively identify vulnerabilities and plan for how a city is growing. Since its inception in 2004, OpenStreetMap (OSM) sets the benchmark for open geospatial data and has become a key player in the public, research, and corporate realms. Following the foundations laid by OSM, several open geospatial products describing the built environment have blossomed including the Microsoft USA building footprint layer and the OpenAddress project. Each of these products use different data collection methods ranging from public contributions to artificial intelligence, and if taken together, could provide a comprehensive description of the built environment. Yet, these projects are still siloed, and their variety makes integration and interoperability a major challenge. Here, we document an approach for merging data from these three major open building datasets and outline a workflow that is scalable to the continental United States (CONUS). We show how the results can be structured as a knowledge graph over which machine learning models are built. These models can help propagate and complete unknown quantities that can then be leveraged in disaster management.en
dc.description.notesNSF, Grant/Award Numbers: OIA #1937099, #2033607en
dc.description.sponsorshipNSF [1937099, 2033607]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1002/aaai.12035en
dc.identifier.eissn2371-9621en
dc.identifier.issn0738-4602en
dc.identifier.issue1en
dc.identifier.urihttp://hdl.handle.net/10919/111291en
dc.identifier.volume43en
dc.language.isoenen
dc.publisherAmerican Association for Artificial Intelligenceen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectFlood impacten
dc.titleKnowledge graphs to support real-time flood impact evaluationen
dc.title.serialAI Magazineen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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Scholarly Works, Civil and Environmental Engineering