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Enviroment Protection

Waste Management Intelligence

Using bus-mounted cameras and AI image recognition, the existing patrol fleet is upgraded into a 24x7 proactive street-cleanliness inspection system. It detects litter and mess on streets, footpaths, parks and bus shelters in real time; the VLM grades the litter (bulky furniture, piled, bagged or scattered) and its setting, automatically assigns the responsible agency (Environmental Protection Bureau, Police, Public Works parks office, Transportation Bureau or property owner) and routes the report, with imagery, time and location retained as evidence for inspection and penalties.

VLM output

A forward-facing dashcam on a bus travels down an urban commercial street in Taiwan, passing a pile of roughly a dozen assorted household garbage bags left uncollected on the sidewalk beside a shuttered storefront at Da Liang Street. The vehicle continues along the same street past parked scooters and shopfronts, with no further waste piles or collection activity visible in the later frames.

  • Illegal Dumping DetectedYes
  • Waste Bag Count10-12
  • Waste LocationSidewalk
  • Overflow LevelModerate
  • Bin Or Container PresentNo
  • Collection Vehicle PresentNo
  • Waste Worker PresentNo
  • Sidewalk ObstructionPartial
  • Waste TypeMixed Household

Customer challenges

1

Street footage is available but litter in it is never recognised

2

Scale and type (bulky, piled, scattered) are not graded, so dispatch is mis-sized

3

Jurisdiction depends on the setting (road, park, bus stop, private land) and is disputed

4

Illegal dumping is found after the fact with no imagery, time or location evidence

5

No hotspot or time pattern to target repeat offenders

Customer benefits

Inspection shifts from citizen reports only to 45 buses inspecting daily; proactive coverage up 188–200x

AI assigns the correct agency by setting, reducing cross-agency disputes

Reporting time cut 90%; litter found 60 minutes earlier than manual reports

Passed official on-site verification with 75% L1 recognition accuracy, meeting the acceptance threshold

Cleaning resources allocated by litter scale, avoiding over- or under-dispatch

Imagery, time and location retained for inspection and penalty evidence

Key features

Street Litter Detection (Bus-Mounted Cameras)

Detects street litter from bus-mounted cameras.

Litter Scale Grading (Bulky, Piled, Scattered)

Grades litter scale as bulky, piled, or scattered.

Jurisdiction Assignment by Setting (Road, Park, Bus Stop, Private Land)

Assigns jurisdiction by setting, such as road, park, bus stop, or private land.

Illegal Dumping Hotspot Monitoring & Evidence Package

Monitors illegal dumping hotspots and compiles evidence packages.

Cleaning Dispatch & Case Closure

Dispatches cleaning crews and tracks cases through to closure.

Before & after

Before 265 fixed sources, about 2.4 reports per day After — with Linker AI Nexa Up to 50,000 video sources, proactive coverage 188–200x
Before Litter reported by citizens hours later After — with Linker AI Nexa Found 60 minutes earlier, reporting time -90%
Before Site visit before dispatch After — with Linker AI Nexa Detection to dispatch ≤ 10 minutes
Before Recognition accuracy unmeasured After — with Linker AI Nexa L1 accuracy 75%, passed official on-site verification
Before Illegal dumping found after the fact After — with Linker AI Nexa Violations at monitored hotspots -80%
Before Jurisdiction disputed case by case After — with Linker AI Nexa 100% auto-assigned by setting

More Scenarios