Port of Rotterdam: Pioneering AI in asset maintenance

• The Port of Rotterdam asset management department spends considerable time and money on visual inspections

• Taking photos of damaged assets, classifying the damage, and uploading findings

• To reduce costs, save man-hours, and increase inspector safety, the "Machine Learning Inspector" project was started

• Car-mounted cameras film the environment as inspectors drive around the port.

• AI processes and analyzes the video to detect and assess asset damage, focusing initially on assets visible from the car, such as traffic lights, corner blocks, and roads.

What was your biggest challenge?

The challenge was translating raw video footage and GPS signals into actionable data for asset managers to deploy repair teams.

What was the solution?

Together with port of Rotterdam data scientists a Dataworkz ML engineer built a scalable video processing and AI platform. We split the analysis into steps: recognizing assets in video footage, attaching GPS coordinates by extrapolating the car’s GPS, and using expert models to assess damage. Results were saved in a database, and a user dashboard was built on top of it.

What was the result?

We successfully transitioned from an experimental phase to a minimal viable product.

What did you learn?

• Cloud-managed machine learning services are great for experimentation but may lack the scalability, cost-efficiency, and customization needed for mature products, challenging performance and reliability at scale.

• Simplifying complex products requires ongoing diligence and thoughtful design, breaking systems into manageable components and maintaining clarity and usability

What did you like best?

The direct link between our work and real-world applications was incredibly rewarding. Going into the port, recording data, testing the pipelines ourselves and automating a complicated manual process that leads to a whole new way of working, is something unique to experience.

"The MLI project takes us a step further in the effective and objective inspection of large quantities of assets. Although it is a long-term process, we are fortunate to see that the journey itself has already led to remarkable and valuable insights. The MLI process brings knowledge areas closer together, which is of great value for a future-proof organization."

Marlot Schoenmaker – Program Manager of Digital Transformation Asset Management