The aim is to allow the city to tackle the issue of waste dumping more efficiently and keep laneways – the busy and narrow city streets and pedestrian areas – safe and clean.

Laneway servicing

The trial uses Nokia Scene Analytics to optimise laneway servicing and understand waste disposal behavioural trends in a busy Melbourne laneway.

To decrease the frequency of waste contractor visits to busy areas, the City has offered local residents and businesses subscription-based access to the large-capacity compactor facilities. With the compactor in place, council then wanted to understand how the service was being utilised and how to mitigate illegal waste dumping, which can quickly create safety and hygiene issues in the area.

Under its “emerging technology testbed” initiative, the City of Melbourne worked with Nokia to leverage an existing network of installed cameras as internet of things (IoT) sensors to monitor one of the compactors. The Scene Analytics solution employed an AI-powered algorithm to filter and collate data from the cameras, while also combining other data sources, such as operational data on the compactor itself, to create real-time alerts and produce reports.

“This innovative project will help to avoid hazards and make our streets even cleaner by allowing our waste services to better understand behaviour trends”

The City reports initial trial results demonstrate that Scene Analytics can support its objectives for better, safer citizen experiences while simultaneously lowering maintenance and down time costs for waste management services.

“This is a great example of using new technology to help remove illegal waste more quickly, make our city cleaner and protect the environment,” said Sally Capp, lord mayor, City of Melbourne.

“Our partnership with Nokia is another way we are gathering data to make Melbourne a safer, smarter and more sustainable city. This innovative project will help to avoid hazards and make our streets even cleaner by allowing our waste services to better understand behaviour trends related to the illegal and dangerous dumping of waste.”

The trial allowed for real-time monitoring and detection of activity in the vicinity of the compactor using a virtual tripwire. Object detection and object counting was used to identify and count items to show how the compactor was impacted by items incorrectly placed within it, while also identifying potentially dangerous items.

Anomaly detection identified unusual movements, such as illegal waste dumping during the night, while face and license plate blurring maintained individual privacy during the trial.

Using these reports, City of Melbourne can better understand the correlation between illegal waste-dumping activities and compactor downtime, to keep maintenance teams better informed and minimise issues. It also allows them to tackle waste dumping activities before they become a hazard, viewing locations in real-time to observe any obstructions to service vehicle access, and adapting their schedule to reduce unnecessary visits and minimise their carbon footprint.

By understanding patterns of compactor usage and waste dumping activities, the City said it is also able to patrol the area more effectively, while developing an ongoing campaign to better inform and educate the community.