Canton chose Cyvl to rapidly capture the entire network with vehicle-mounted LiDAR, HD imaging, and sensors, scanning 95.5 roadway miles and cataloging 3,265 signs.

Canton’s road network faced rising resident complaints about potholes, uneven pavement, and outdated signs, while budgets were tight and crews were stretched thin. Pavement condition inventories were slow, manual, and expensive—often relying on staff in trucks with clipboards—making it hard to prioritize and schedule work quickly. Without trustworthy, comprehensive data, leaders struggled to link reported defects to a clear plan that moved from budgeting to engineering to construction fast enough to meet community expectations.
Canton chose Cyvl to rapidly capture the entire network with vehicle-mounted LiDAR, HD imaging, and sensors, scanning 95.5 roadway miles and cataloging 3,265 signs. Cyvl’s Infrastructure Intelligence platform used AI to transform this field data into detailed, actionable pavement condition assessments and engineering-ready maps, so decision-makers could create a comprehensive plan in less time. Delivered on 2025-06-18, the results provided precise segment-level recommendations and costs that let budget, engineering, and operations teams coordinate and get work started quickly for the community.
With detailed, actionable pavement condition data for all 95.5 miles, Canton linked 311-style reports to clear priorities and moved from planning to construction faster. The city began scheduling construction and maintenance projects starting in weeks from receiving Cyvl data, not months, directly reducing the time residents wait for safer, smoother streets. By streamlining decisions and sharpening scopes, Canton can maintain or repave 5x the number of roads compared to traditional methods, improving safety for everyone while maximizing taxpayer value.