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 socio- demographic data) that can be used for optimal
scheduling of asset maintenance and repair
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