Knowledge Graphs to Support Real-Time Flood Impact Evaluation

Authors

  • J. Michael Johnson University of California, Santa Barbara
  • Tom Narock Goucher College
  • Justin Singh-Mohudpur University of California, Santa Barbara
  • Doug Fils Consortium for Ocean Leadership
  • Keith C. Clarke University of California, Santa Barbara
  • Siddharth Saksena Virginia Polytechnic Institute and State University
  • Adam Shepherd Woods Hole Oceanographic Institution
  • Sankar Arumugam North Carolina State University
  • Lilit Yeghiazarian University of Cincinnati

DOI:

https://doi.org/10.1002/aaai.12035

Abstract

A 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.

The NSF Convergence Accelerator, by James Gary

Downloads

Published

2022-03-31

How to Cite

Johnson, J. M., Narock, T. ., Singh-Mohudpur, J. ., Fils, D. ., Clarke, K. ., Saksena, S. ., Shepherd, A. ., Arumugam, S. ., & Yeghiazarian, L. . (2022). Knowledge Graphs to Support Real-Time Flood Impact Evaluation . AI Magazine, 43(1), 40-45. https://doi.org/10.1002/aaai.12035

Issue

Vol. 43 No. 1: Spring 2022

Section

Special Topic Articles

License

Copyright (c) 2022 AI Magazine

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
 
Authors who publish with this journal agree to the following terms:
  1. The author(s) warrants that they are the sole author and owner of the copyright in the above article/paper, except for those portions shown to be in quotations; that the article/paper is original throughout; and that the undersigned right to make the grants set forth above is complete and unencumbered.
  2. The author(s) agree that if anyone brings any claim or action alleging facts that, if true, constitute a breach of any of the foregoing warranties, the author(s) will hold harmless and indemnify AAAI, their grantees, their licensees, and their distributors against any liability, whether under judgment, decree, or compromise, and any legal fees and expenses arising out of that claim or actions, and the undersigned will cooperate fully in any defense AAAI may make to such claim or action. Moreover, the undersigned agrees to cooperate in any claim or other action seeking to protect or enforce any right the undersigned has granted to AAAI in the article/paper. If any such claim or action fails because of facts that constitute a breach of any of the foregoing warranties, the undersigned agrees to reimburse whomever brings such claim or action for expenses and attorneys’ fees incurred therein.
  3. Author(s) retain all proprietary rights other than copyright (such as patent rights).
  4. Author(s) may make personal reuse of all or portions of the above article/paper in other works of their own authorship.
  5. Author(s) may reproduce, or have reproduced, their article/paper for the author’s personal use, or for company use provided that original work is property cited, and that the copies are not used in a way that implies AAAI endorsement of a product or service of an employer, and that the copies per se are not offered for sale. The foregoing right shall not permit the posting of the article/paper in electronic or digital form on any computer network, except by the author or the author’s employer, and then only on the author’s or the employer’s own web page or ftp site. Such web page or ftp site, in addition to the aforementioned requirements of this Paragraph, must provide an electronic reference or link back to the AAAI electronic server, and shall not post other AAAI copyrighted materials not of the author’s or the employer’s creation (including tables of contents with links to other papers) without AAAI’s written permission.
  6. Author(s) may make limited distribution of all or portions of their article/paper prior to publication.
  7. In the case of work performed under U.S. Government contract, AAAI grants the U.S. Government royalty-free permission to reproduce all or portions of the above article/paper, and to authorize others to do so, for U.S. Government purposes.
  8. In the event the above article/paper is not accepted and published by AAAI, or is withdrawn by the author(s) before acceptance by AAAI, this agreement becomes null and void.