Smart Data Management of Urban Infrastructure Using Geographic Information Systems
Publication: Journal of Infrastructure Systems
Volume 26, Issue 4
Abstract
Cities all over the world are converting maps of their infrastructure systems from legacy formats [such as paper maps and computer-aided design (CAD) drawings] to geographic information systems (GIS). Compared with CAD, GIS tend to offer more flexibility in terms of managing, updating, analyzing, and processing data. Nonetheless, the conversion process to GIS can be extremely challenging from a technical point of view. Moreover, the original data in a legacy format often contain errors, and pieces of infrastructure are often missing. What is more, even once the conversion process is complete, the maintenance of the data and the fusion of the data set with other data sets can be challenging. Leveraging recent technological advances (such as machine learning and semantic reasoning), this paper proposes a framework to better manage infrastructure data. More specifically, a smart data-management protocol is presented to successfully convert infrastructure maps from CAD to GIS that includes a data-cleaning procedure in CAD and machine-learning algorithmic solutions to validate or suggest edits of the infrastructure once converted to GIS. In addition, the protocol includes elements of version control to keep track of how urban infrastructure evolves over time as well as a procedure to combine GIS infrastructure maps with other data sets (such as sociodemographic data) that can be used for optimal scheduling of asset maintenance and repair.
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Data Availability Statement
No data, models, or code were generated or used during the study.
Acknowledgments
The authors thank the following students for the preliminary work related to this framework: Omar Belingheri, Guillemette Fonteix, Nalin Naranjo, and Eric Boria. The authors are also indebted to coinvestigators Michael Siciliano, Roberto Tamassia, and Goce Trajcevski for many helpful discussions. This work was partially supported by NSF awards CNS-1646395, CAREER 155173, III-1618126, and CCF-1331800.
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Published In
Journal of Infrastructure Systems
Volume 26 • Issue 4 • December 2020
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Jan 4, 2020
Accepted: Jul 21, 2020
Published online: Sep 26, 2020
Published in print: Dec 1, 2020
Discussion open until: Feb 26, 2021
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