The problem analysis, planning and monitoring phases of sustainable urban management policies require reliable information on the urban environment and its dynamics. Geospatial and socio-economic data supplemented with knowledge on dynamic urban processes are incorporated in the land-use change models currently available to planners and policy makers. They enable them to assess the impacts of decisions on the spatial systems that they are to manage. To be usefully applicable the models need extensive calibration. Current calibration methods, however, do not take into account uncertainties in reference land-use data and uncertainties in the parameterization of land-use change models. As a result, uncertainty in land-use change predictions are mostly unknown. The ASIMUD project aims to provide a solution to this issue by applying a particle filter data-assimilation framework to the calibration of land-use change models. The framework will use remote sensing derived land-use data at time steps that they are available in order to optimize the parameters in the model. The proposed calibration framework will be based on the comparison of spatial metrics derived from historic remote sensing images and land-use change simulation results. Parameters used in the simulation model will be tuned so that the simulated urban growth patterns, as described by the metrics, match the patterns observed in the remote sensing imagery. It is expected that the approach will result in a quantification and reduction of the uncertainty in simulations of future land use.