City considers street‑level imagery, AI asset scanning to prioritize repairs and reduce liability

Hurricane City staff described a vendor pitch and pilot price for a street‑level imagery service that would capture high‑resolution imagery, LiDAR and AI-scored pavement ratings. GIS specialist Ben Holmes and other staff told council members the tool would create a searchable, historical record of streets, sidewalks and visible utilities.

Ben Holmes said the vendor collects imagery and applies automated analyses — for example, a pavement-condition score and sidewalk/curb trip‑hazard detection — and then delivers data that can be imported into the city's GIS for asset management. "They outlined, I talked to them. They are fine holding to 21,150 for just 1 year. So that we could test it and see. They'll stand by that number. It won't go up if we don't do the 5 year contract," Holmes said, describing an option to start with a one‑year pilot.

Staff framed multiple benefits: faster field diagnostics, a searchable historical imagery archive to respond to liability claims and an AI tool that can flag trip hazards so crews can prioritize repairs. An attorney in the meeting cautioned that documenting more defects could increase short‑term repair obligations or exposure, but other staff and the city’s insurer advised that logging and showing an active mitigation plan can reduce liability risk.

Staff said the product would be useful across departments — streets, power and water — and could populate worklists (e.g., downstream assets affected by a valve closure) and integrate with the city's GIS work‑order processes. City staff also suggested using the images at periodic intervals (yearly or multi‑year) to build a historical dataset.

No contract was approved at the meeting; staff asked council to consider pilot funding and return with refined budget numbers.