Eliminating the As-Built Update Backlogs by Automating Field Data Capture

A longstanding and unsolved problem for utility companies has been how to keep geospatial network asset data up to date as networks are extended and maintained. Currently, it can take weeks if not months for “as-built” changes to make their way back into the enterprise system of record, typically a geographic information system (GIS). 

Existing mobile solutions can enable workers in the field to capture data about network asset data updates, but are too time consuming and complex for field workers in many cases. 

However, a range of new technologies including include computer vision, machine learning and augmented reality can make this data capture process much more automated, simpler and faster. This presentation looks at how to apply these technologies to dramatically improve data quality and currency in utilities, enabling the vision of a “digital twin” of a utility’s network, which many companies are striving for.