Predicting ground vehicle performance requires in-depth knowledge, captured as numeric parameters, of the
terrain on which the vehicles will be operating. For off-road performance, predictions are based on rough terrain
ride comfort, which is described using a parameter entitled root-mean-square (RMS) surface roughness. Likewise,
on-road vehicle performance depends heavily on the slopes of the individual road segments. Traditional methods
of computing RMS and road slope values call for high-resolution (inch-scale) surface elevation data. At this
scale, surface elevation data is both difficult and time consuming to collect. Nevertheless, a current need exists
to attribute large geographic areas with RMS and road slope values in order to better support vehicle mobility
predictions, and high-resolution surface data is neither available nor collectible for many of these regions. On the
other hand, meter scale data can be quickly and easily collected for these areas using unmanned aerial vehicle
(UAV) based IFSAR and LIDAR sensors. A statistical technique for inferring RMS values for large areas using
a combination of fractal dimension and spectral analysis of five-meter elevation data is presented. Validation of
the RMS prediction technique was based on 43 vehicle ride courses with 30-centimeter surface elevation data.
Also presented is a model for classifying road slopes for long road sections using five-meter elevation data. The
road slope model was validated against one-meter LIDAR surface elevation profiles. These inference algorithms
have been successfully implemented for regions of northern Afghanistan, and some initial results are presented.