Steven Braakman
Expert Infra
Road Data. Every route. Every detail.
Accurate, attribute-rich road data across the full network. Ready for infrastructure design, traffic safety analysis and route risk assessment.
What we monitor
We detect and classify road infrastructure across the full Dutch road network. From national highways and municipal streets to rural lanes and unpaved tracks. We map physical road characteristics including surface type, road layout features, roadside obstacles and contextual elements such as trees, poles, slopes and adjacent structures. Change monitoring tracks modifications to road composition and roadside context over time, ensuring that infrastructure analysis is always based on current conditions rather than outdated registrations.
Methodology & Technology
We combine aerial photography, satellite imagery, BGT topographic data and AHN elevation models with object detection and AI-driven classification to map road surfaces, features and roadside context at segment level. Road characteristics are automatically detected. Crosswalks, speed bumps, pavement types and obstacle zones are identified through image analysis and validated against reference data. All output is linked to specific road segments, enabling direct integration with GIS environments, accident databases, traffic sign datasets and infrastructure design tools. The dataset is updated in line with new aerial coverage cycles, with change detection applied to identify material modifications in road composition and context.
Examples of what we detect
Road surface type classifies the material covering each road segment. Asphalt, concrete, brick paving, gravel or unpaved. It is one of the most significant cost drivers in infrastructure projects, as different surface types carry substantially different removal and restoration costs when excavation is required.
Road width records the measured carriageway width per segment. It determines design constraints for new infrastructure routing, dictates applicable traffic safety norms and is a key variable in assessing whether a road can absorb changes in use. Such as the addition of a cycle lane or the rerouting of heavy vehicles.
This attribute identifies the location and type of pedestrian crossing per road segment. Crosswalks are a primary traffic safety indicator. Their presence, type and condition directly affect pedestrian risk and are a standard input for road safety audits and intervention prioritisation.
Speed bumps and related traffic calming measures are detected and located per road segment. Their presence, or absence, is a significant explanatory variable when combining road data with accident records to identify root causes of unsafe locations.
Roadside obstacles records the presence, type and proximity of objects in the direct verge of the road — including trees, utility poles, embankments and buildings. Obstacles in the road zone are a leading cause of single-vehicle accidents, and their proximity to the carriageway directly informs safety risk scoring.
Slope geometry captures the elevation profile of the road verge, identifying where a vehicle leaving the road would encounter a sudden drop, embankment or water feature. Slope risk is an underused but evidence-based predictor of accident severity at rural and semi-rural locations.
Root zone extent records the estimated below-ground footprint of trees adjacent to road infrastructure. Root zones directly constrain where cables, pipes and foundations can be placed. Making this attribute essential for infrastructure route design and cost estimation before ground is broken.
Output & integration
Road data is available via API for direct integration into GIS environments, engineering design tools and traffic analysis platforms, and as geodata exports per region or the full Netherlands. All data is connected at road segment level, enabling spatial queries such as identifying all routes with tree root zones within two metres of a planned cable corridor, or all crosswalks in a municipality without speed reduction infrastructure nearby. The dataset combines directly with tree data for infrastructure route risk analysis, with impervious surface data for surface cost estimation, with building data for urban route corridor analysis, and with accident datasets for traffic safety root cause modelling.
Connected Intelligence
Challenge us.
We work with organisations that have specific, complex or non-standard data needs and we like it that way. Tell us what you’re looking for.
Explore our road data and it’s possibilities.
Steven Braakman
Expert Infra
