Geographic information systems’ ability to effectively manage and analyze spatial data makes it an ideal means for cities to manage their infrastructure systems. However, converting from paper maps and computer-aided design (CAD) drawings can be challenging with legacy data that often contains errors or potential missing data. A new paper in the Journal of Infrastructure Systems, “Smart Data Management of Urban Infrastructure Using Geographic Information Systems,” by Booma Sowkarthiga Balasubramani; Mohamed Badhrudeen, A.M.ASCE; Sybil Derrible, A.M.ASCE; and Isabel Cruz, presents a smart data-management framework to successfully convert infrastructure maps from CAD to GIS.
Cities all over the world are converting maps of their infrastructure systems from legacy formats, such as paper maps and computer-aided design drawings, to geographic information systems. 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.
Read the full paper free in the ASCE Library: https://doi.org/10.1061/(ASCE)IS.1943-555X.0000582