With trustworthy, current data in hand, Duluth moved from reactive firefighting to a confident, proactive program that residents could see on the street.
Duluth’s freeze–thaw cycles and steep grades accelerated pavement wear, yet the City was operating with outdated and inconsistent condition information that made planning guesswork. Crews relied on manual windshield surveys and anecdotal reports, which were slow, unsafe, and often inconsistent, leaving leaders without sufficient data to make informed decisions and act fast. Without a clear, defensible prioritization method, staff were stuck answering “Why not my road?” at town meetings while reacting to 311 complaints instead of executing a strategic plan.
Duluth selected Cyvl to rapidly survey the entire network with vehicle-mounted LiDAR and high-resolution sensors, covering 458 roadway miles and capturing lane-level surface condition and geometry. Within weeks, and by October 25, 2024, Cyvl’s Infrastructure Intelligence platform used AI to convert the data into detailed pavement condition scores, asset inventories, and map-based reports. The City received prioritized repair lists and defensible, multi-year work plans tied to realistic budgets, all grounded in 72,004 mapped assets, including 60,209 boulevard features and 11,795 sidewalk segments.
With trustworthy, current data in hand, Duluth moved from reactive firefighting to a confident, proactive program that residents could see on the street. Engineering and Public Works scheduled resurfacing and maintenance sooner because the analysis arrived in weeks, not months, shrinking the gap between data collection and project launch. Leaders now communicate the plan with clear maps and metrics, improving transparency, focusing crews on the highest-priority segments, and directing taxpayer dollars where they deliver the most community value.