---
title: "Street asset defect reports from routine drives: AI Use Case"
description: "Use images captured on routine drives to flag likely road, sign and street-furniture defects with a location, so an inspector confirms each one and real defects become work orders."
url: https://lidvizion.ai/use-cases/street-asset-defect-reports/
site: Lid Vizion
index: https://lidvizion.ai/llms.txt
---

[Use cases](https://lidvizion.ai/use-cases/) · Facilities & operations

# Street asset defect reports from routine drives

Use images captured on routine drives to flag likely road, sign and street-furniture defects with a location, so an inspector confirms each one and real defects become work orders.

[Talk through this workflow](https://lidvizion.ai/contact/?need=milestone-delivery&from=%2Fuse-cases%2Fstreet-asset-defect-reports%2F#write) · [Build the missing piece](https://lidvizion.ai/services/custom-software/)

Where it starts

## The trigger.

A vehicle with a mounted camera completes a routine route, such as a waste, transit or patrol run.

How it runs today

- Defects are found when someone complains or happens to notice them.
- Inspection drives are infrequent and notes are hard to compare over time.
- Reports arrive without a precise location or photo.

The workflow

## Four bounded steps.

Each step has an owner and a stop rule.

1. 01 / Capture
   Record images with location from cameras on vehicles that already drive the routes.
2. 02 / Flag
   Flag likely defects such as potholes, damaged signs or obstructions, with a photo and location.
3. 03 / Confirm
   An inspector reviews each flag and confirms, rejects or reclassifies it.
4. 04 / Act
   Confirmed defects become work orders with priority, and repeat locations are tracked.

Where people stay in control

## Approval points.

- An inspector confirms every flag before a work order is created.
- Faces and number plates are blurred before images are stored or reviewed.

How we would measure it

## Against a baseline.

Measured before and after, on the same work.

### Share of flags confirmed by inspectors.

### Time from capture to work order for confirmed defects.

### Defects found by routine drives versus by complaints.

Related

## Go deeper.

- [Build the missing piece](https://lidvizion.ai/services/custom-software/): Custom tools and applications for the gaps that matter.
- [Edge vs. cloud deployment](https://lidvizion.ai/resources/basics/edge-vs-cloud-deployment/): Where a model should run: next to the camera or sensor, or in a data centre.
- [Find street defects from routine drives](https://lidvizion.ai/guides/find-street-defects-from-routine-drives/): A four-step playbook to choose the defects worth finding, capture images on routes your vehicles already drive, have an inspector confirm every flag and turn confirmed defects into work orders.
- [How to find road and street defects with computer vision](https://lidvizion.ai/answers/how-to-find-road-and-street-defects-with-computer-vision/): How can a city, county or utility use cameras on vehicles that already drive the routes to find potholes, damaged signs and other street defects sooner?

More use cases

## Other places to start.

[All use cases](https://lidvizion.ai/use-cases/)

- [Parking and space occupancy from cameras](https://lidvizion.ai/use-cases/parking-and-space-occupancy/): Facilities & operations Use existing cameras to count which spaces are free or occupied and feed the count to signage, an app or a dashboard, with people checking anything unusual.
- [Facilities work order intake and dispatch](https://lidvizion.ai/use-cases/facilities-work-order-intake/): Facilities & operations Turn maintenance requests from email, forms and chat into complete work orders, set a priority, and route each one to the right technician or vendor for approval.
- [Daily work order brief](https://lidvizion.ai/use-cases/daily-work-order-brief/): Facilities & operations Summarise open work orders each morning, highlighting overdue, blocked and safety-related jobs and what is waiting on parts, permits or access, so supervisors start the day with a short list of what needs a decision.

An illustrative workflow pattern, not a result from a specific customer.

Bring the messy version

## One workflow. A better way to run it.

Show us the tools, the handoffs, and the part that keeps slowing your team down.

[Let’s map your workflow](https://lidvizion.ai/contact/) · [Read our approach](https://lidvizion.ai/approach/)