By June 25, 2024, the city had detailed, actionable pavement condition data for every one of the 90 roadway miles surveyed, cutting months from the typical timeline.
Shoreview faced accelerating wear from Minnesota freeze–thaw cycles, but the city’s pavement data was outdated and inconsistent, making it hard to know which roads to fix and when, and impossible to build accurate paving budgets. Without a defensible prioritization method, staff were pulled into reactive responses to complaints and struggled to explain choices at council and neighborhood meetings. Manual assessments took too long, were inconsistent across crews, and by the time reports were compiled, the information was already stale for decision-making.
Shoreview chose Cyvl to rapidly survey the full network using vehicle-mounted LiDAR and sensors, capturing objective pavement conditions across 90 roadway miles in a matter of weeks. Cyvl’s Infrastructure Intelligence platform used AI to convert the raw scans into street-by-street pavement condition scores, maintenance and rehabilitation recommendations, and ready-to-use budget scenarios that align with engineering best practices. The city received a transparent, defensible plan, complete with prioritized repair lists and maps, enabling leaders to brief council and the public with confidence and move to action faster.
By June 25, 2024, the city had detailed, actionable pavement condition data for every one of the 90 roadway miles surveyed, cutting months from the typical timeline. Public Works leveraged the plan to schedule near-term maintenance and program multi-year investments, so residents see faster repairs and clearer rationale for what happens next. With credible, shared data, council conversations are simpler, budget reviews are straightforward, and staff can address the highest-priority segments first based on transparent criteria.