A quantitative assessment of the risk of pluvial flooding in an urban area is subject to various uncertainties. These include evaluating the intensity and patterns of rainfall across the area, and the exposure and specific vulnerability of different targets. In this study, the impact of rainfall pattern and intensity description is evaluated using rainfall information obtained from innovative opportunistic sensors based on satellite links and traditional rain gauges, located both within and outside the flood-prone area. Urban Flood Drifters (UFDs) are considered the primary targets, with a focus on motor vehicles (cars, vans, and motorbikes). Aerial images and deep learning–based object detection are used to evaluate the exposure of potential UFDs in terms of their numerosity and location within flood-prone urban areas. The specific vulnerability of these targets is considered using stability and damage curves from the literature, based on water depth and flow velocity. The magnitude and spatial distribution of these flood characteristics are obtained by running hydrodynamic simulations of two-dimensional pluvial flooding propagation in the urban area, based on rainfall patterns obtained from both opportunistic and traditional sensors. The impact is demonstrated in terms of vehicle safety, monetary damage experienced by flooded vehicles, and traffic disruption using an observed event. The results obtained from different rainfall data sources and aerial images with different spatial resolutions are compared and discussed in terms of their suitability for object detection and risk assessment. Flood hazard maps demonstrated that rain gauges positioned outside of the study area do not capture the actual magnitude of the event measured at the reference station. On the contrary, opportunistic sensors, despite significant underestimation of the peak rainfall, provide a good representation of the temporal evolution and total volume of the reference rainfall event. Damage scenarios demonstrated that the role of false negatives, in automated object detection, can be relevant to the safety analysis and the assessment of monetary damage, especially when spatial resolution is coarser, while it is quite negligible for the assessment of traffic disruption.