Real-World Challenges in Geospatial Machine Learning

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Join us at TomTom’s Amsterdam office on Thursday, July 9, for an evening dedicated to the practical challenges of building geospatial machine learning systems.

Machine learning is a powerful tool, but applying it to real-world geospatial data presents a unique set of challenges. In this meetup, we are bringing together research and engineering perspectives to discuss how to overcome these hurdles, design robust pipelines, and maintain data quality in a rapidly changing world.

Agenda

17:30 - 18:25: Welcome with food and drinks! 18:25 - 18:30: TomTom’s intro
18:30 - 19:00: Talk 1: Designing ML systems for continuously changing world, or how does Automated Driving really work, by Ahmed Boudissa 19:00 - 19:35: Talk 2: 85 million matches, thousands of catches: active learning at production scale, by Haris Iqbal
19:35 - 19:45: Break 19:45 - 20:15: Talk 3, by Ioanna Micha & Nathalie Dees

• 20:15 - 21:00: Networking / drinks

Talk 1: Designing ML systems for continuously changing world, or how does Automated Driving really work, by Ahmed Boudissa

Building machine learning systems is one thing. Building systems that remain accurate, fresh, and scalable while the real world changes every day is another challenge entirely.

In this talk, we'll explore how TomTom turns billions of raw observations from satellites, survey vehicles, connected devices, and onboard sensors into lane-level map products used by automated driving systems. Rather than focusing on a single model, we'll look at the broader system design challenges involved in combinin