European cities face growing pressure to cut transport emissions and adapt mobility systems to net-zero targets. Planners and authorities face:
- Poor spatial resolution on transport emissions: City-level emission inventories rarely capture where emissions are highest or how they vary with mobility patterns across the urban area.
- Sparse air quality monitoring: Ground stations are not dense enough to support district-level analysis of the link between transport activity and air quality outcomes.
- Uncertainty in long-term urban growth: Sustainable mobility planning requires projections of where people will live and travel over 10-20 year horizons, but land use change is hard to model without reliable input data.
- Infrastructure maintenance backlogs: Transport agencies struggle to prioritise maintenance across large road and rail networks without systematic condition monitoring at scale.
- Monitoring and reporting gaps: Sustainable Urban Mobility Plans (SUMPs) and EU climate commitments require repeatable, comparable indicators that conventional data sources cannot always provide at the required frequency.
How EO can help
Satellite data provides the spatial coverage and update frequency that ground-based systems cannot match:
- Transport-related air quality mapping: Sentinel-5P NO2 and aerosol data links mobility corridors to air quality outcomes at city and regional scale.
- Urban growth and land use modelling: Time-series analysis of Sentinel-2 imagery maps how cities are expanding and where future mobility demand is likely to concentrate.
- What-if scenario analysis: EO-derived land use and population data feeds AI-driven models that simulate future urban growth and assess the mobility implications of different planning decisions.
- Infrastructure condition monitoring: SAR-based deformation and change detection supports predictive maintenance planning for roads, bridges, and other transport assets.
- SUMP monitoring: Satellite-derived indicators (land use change, transport corridor density, urban green cover) provide repeatable, comparable metrics for tracking progress against mobility and climate plans.
Key examples
1- CITYNEXT
CITYNEXT is an ESA-funded project that combines EO with AI to model urban growth over a 10-year horizon and assess the mobility implications of different development scenarios. The project generates what-if analyses that link land use change to transport demand, urban climate effects, and air quality outcomes. City planners can use the scenarios to evaluate the likely effects of policy decisions before committing to infrastructure investments.
2- Green Transition Information Factories (GTIF) Austria
The GTIF Austria platform includes services that use EO data to analyse the relationship between mobility patterns and air quality across Austrian cities. A separate service supports predictive maintenance of transport infrastructure by integrating satellite-derived monitoring data with asset management workflows. Both services are operational and accessible to public authorities via the GTIF platform.
Figure: Deformation Monitoring Service of Critical Infrastructure for Transport Infrastructure Predictive Maintenance In Austria (Source: GTIF Austria)
3- Air quality and traffic monitoring in Sarajevo
As part of the EBRD Green Cities Programme in Bosnia’s Sarajevo region, an ESA Global Development Assistance project delivered three EO-based services: air quality analysis using CAMS data and Sentinel-5P data for six key pollutants, and a cross-correlation with traffic and public transport data. Machine learning was used to downscale satellite data to 1 km resolution. Outputs fed into an interactive dashboard for EBRD and local stakeholders to assess the relationship between transport activity and urban air quality.
Figure: Number of days when PM2.5 concentrations exceeded the WHO daily thresholds in Sarajevo to examine the correlation between air quality and traffic data (Source: GDA ESA)
4- Scope 3 freight emissions monitoring (Ovinto)
Scope 3 is an ESA-funded project that uses satellite positioning (GNSS) data to monitor greenhouse gas emissions from freight transport by rail, truck, and barge. It includes a modal shift simulator that compares emissions across transport modes and feedstocks, giving shippers and logistics operators the evidence base they need for corporate sustainability reporting.
Figure: Technical data flow in Scope 3
Further resources
