Geospatial systems & AI for the environment
I build optimization models, machine-learning pipelines and web tools — not just maps. Turning spatial data into environmental decisions, entirely in open-source Python, from architecture scale to the scale of a coastline.
Linear & mixed-integer models that allocate land, energy and habitat under hard constraints.
Machine learning on satellite & aerial imagery — segmentation, detection and change over time.
High-performance transit engines on real timetable data to measure access and modal shift.
FastAPI services, data pipelines and interactive dashboards that put analysis in people's hands.
My route into geospatial work ran through the field — three Arctic and Subarctic seasons mapping ground and water, and a master's in stable-isotope hydrology spent chasing meltwater across the Mackenzie Delta. That work made one thing obvious: the hard part is rarely the map. It is the system behind it — the model, the pipeline, the optimization that turns a pile of measurements into a decision someone can defend.
So I build those systems. Today I work as one of two geospatial specialists at an architecture and urbanism practice, in a Python-first, fully open-source stack that bridges building scale to city and regional scale. The thread running through all of it is environmental: renewable-energy siting that respects ecology, mobility analysis that nudges cities off cars, remote sensing that tracks the things worth protecting.
I'm based in Ghent, work in English and French, and I'm steadily wearing down Dutch. When I'm not at a terminal you'll find me on the water — a long habit from competitive rowing — or building small side projects that have no business being as over-engineered as they are.
A linear-optimization engine balancing renewable energy, marine habitat and food production across seven countries' waters.
A vectorised RAPTOR router over GTFS timetables, scoring access and equity for a proposed interborough corridor.
A segmentation model that delineates individual urban tree crowns from aerial imagery to map canopy and its gaps.
A FastAPI tool that ingests open building data and returns environmental and energy performance for any urban block.